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What Happens When A Bass Head Interviews Armin Van Buuren About Trance [Exclusive] – Your EDM

Posted: February 14, 2017 at 11:33 am

What happens when a bass head interviews Armin van Buuren about trance? Actually, something pretty magical.

As a bass head, Im used to interviewing bass artists and producers in similar veins of music, and as such, the topics can sometimes become quite repetitive; realistically, the bass music world is pretty small. But when you put me in a room with someone like Armin van Buuren, whos been in the music industry for 20 years, founder of Armada and A State Of Trance, one of the biggest DJs in the world, Im going to have a lot to ask.

My interview with Armin came two days after I saw hisArmin Only set at the Forum in Inglewood, certainly one of the largest productions Ive ever seen for a solo DJ performance. Beyond the five-hour set time, the entire night was marked by incredible theater production and acrobatic performances from dancers on stage, as well as appearances from a variety of guest vocalists, many of whom were featured on Armins latest album,Embrace.

Though I arrived nearly two hours after the show had started, it was still well in full swing, with the massive swell of chords behind Adagio For Strings playing as we were finding our seats. Live trumpeterEric Vloeimans was on stage holding a note, while a ballerina danced behind him, telling an intense love tale and from there, the night continued to inspire.

I met up with Armin at the office of his theater and production designer in Venice on Monday, and we spoke about many things, as candidly as possible. With him, I didnt want our interview to be some regurgitated questions from a notepad on my phone; it ended up being a 30 minute conversation in which I learned a lot.

Check it out below.

Armin: *laughing* Yeah no of course. Im all for freedom of speech and freedom of opinion.

Armin: Well it depends on what kind of show you want to see from me. I mean if you come to A State of Trance (ASOT), obviously its pure about trance, its more about the melody, more about the classical Armin sound I guess. Armin Only for me is Id say like Christmas Dinner with Armin Van Buuren. Who doesnt want to have a Christmas Dinner?

If you look at my history, Ive never been just about trance. Even though a lot of people call me just a trance DJ, and if you want to put a label on my forehead, please put trance on there. But Ive never looked at just one genre or style, I like to mix it up, I like to be creative with the term. Some trance fans hate me for it, but I like to be a little more eclectic. Not so much for my fans per-say but also to keep it exciting for myself. Im a big dance music lover, I love a lot of bass stuff. Im a massive drum n bass fan, I love techno, you know? I think at the end of the day well all die techno DJs. *laughing* I mean, my studio partner Benno [de Goeij] always says we start with trance and we end up making white noise because were old. *laughing* Its probably very true. You know, what I try to do with Armin Only is try to go to the next level you know? With the production, the show, I think that there is a lot of undiscovered territory between the world of theater and the world of dance music.

Armin: Yeah well its, you know, I think there is a next level in dance music to be taken. Obviously its about the music, and the music is the most important thing, but I think you can enhance. We can learn a lot from the world of theater and live performance. I dont want to say that all of a sudden dance music has to be played live by live instruments, but you can really enhance a performance by having good choreographed singers, dancers, visuals, lasers and everything else coming together. So to me its a great creative set up. Financially, it was the worst decision ever, too. *laughing* But creatively its inspiring to work with people that dont come from dance music at all. For example, the trumpet player, Eric Vloeimans, if you Google him youll see that hes one of the worlds most renowned trumpet players.

Armin: Yeah! Hes extremely talented. He comes from a world of a lot of jazz musicians, he plays with classical acts. He never does anything with dance music, at all. If you google him youll see that he has a massive name to hold up. He played with a lot of great jazz musicians. It shows you also that Im a massive fan of jazz music and I want to try and incorporate the sound of the trumpet into my sound. Which is sort of, uncharted territory. Its kind of exciting to go out of your way a little bit. Not to say Oh look how cool I am, Im trying new things. Its more for keeping it exciting for myself, because it is very You know, no matter what musical genre youre in, its very tempting to just stay within your own safe path and a lot of these DJs do that.

Thats not criticizing them, thats fine. If thats your way, thats great, but for me I cant do the same thing over and over again. So for me, trance has never been this [puts hands close together] or this [spreads hands far apart], it has always been very varied. Theres very different tastes in trance music, just like there is a lot of different tastes in bass music you know? You might like the one artist in bass music, you might not like the other types of bass music, so yeah its just trying to give people a little more. Its definitely very different from a normal DJ set, like I played at Create at the after party, thats just me with a bunch of music and two decks. Great, love that, and I will always keep doing that, I love the old school way of DJing, but with Armin Only Im trying to move forward.

Technologically, you know we designed this whole time code system, which is the back end of this. Its still freestyle DJ sets, so the show you saw, of course, the individual tracks were choreographed and they were planned, but when I play those tracks its still random. So the set I played in Oakland was different than the set I played at the Forum. Im not playing exactly the same set. So that makes it exciting for me, it makes it exciting for the crew, like if someone gives me a stick with a track five minutes before the show I want to be able to play it. I think thats the essence of DJing. So its exciting to try and find a mix between pre-programmed stuff, like obviously the intro is pre-programmed, but still theres live elements to it, theres the classical ballet, theres the trumpet and theres the live snare roll. I find it exciting to try and see where the the two worlds can meet.

Armin: Yeah not for this show but I use them a lot.

Armin: Well I actually toured for two and a half years with the Myobracelets, it was a great experience because you can control the lights with that, and its fun to point at people out in the crowd. Now I use the drum computer, the Pioneer, to add drums to my set which is exciting, and we have the time code system.

Armin: Uhm I really like the collaboration between me and Vini Vici, I never thought that the psytrance world was so specific and its almost like a religious way of producing like I only heard about that in the world of hardstyle, I mean those guys that are real hardstyle producers, they can probably talk to you about kick production for about four days non-stop. Its all about the kick, hardstyle is about the kick. Its not even the low end, its about the mids and the way it has to be distorted and the way it has to be just right and in the psytrance world its even more

So one of the most exciting things is that even though Im forty years old now and in the industry for twenty years Im still learning every day. You know working with Eric Vloeimans, jazz trumpet player, or working with Kensington, a rock band, they come from a completely different world. You know I was in the studio in Miami with Kensington, you know, mixing Heading Up High and we went from mixing dance stems to drums, and how to crossfade that. It took a year to mix that track, just to get all the parts so that everybody was satisfied; so the band, the management, my management, you know the producer that I work with. Its incredibly satisfying to sort of constantly also as a producer reinvent yourself and I think that is the most fun that I have had in my career since last year. You know being in LA, working with a lot of different artists and working with a lot of different people is just inspiring, I find it very very inspiring.

Armin: No there have been DJs that have had similar high numbers. I wanna stress that Ive never done it as a competition.

Armin: Its more like every week I find inspiration from these new tracks that I find online and you know it used to be vinyl. When I started the show it was all vinyl. Now its online and Soundcloud and all that. I told myself Im not doing A State of Trance just for the sake of high numbers. Every week theres a track that inspires me. And now, actually finally enough, I went back with the radio show to do its original formula. I missed doing that.

A State of Trance radio was actually fired from the radio station it was on at Episode 186 because they moved into a different direction with the radio station, and thats when I started doing Ableton mixes which is back in 2003 when Ableton was still in 2.0 or 3.0. So I was one of the first to do that and I really loved it. I thought it was great because I was able to do the radio show in different languages, but I missed the connection with the music because basically what I was doing was: I was skipping through the tracks and I was only listening to the beginning and end of every track to mix them. I missed listening to the entire track, mixing them live, sort of have that live radio feel and now everybody does podcasts in Ableton. Which is fine, nothing wrong with that again, not criticizing but I dont think thats making radio, at least in my mind.

You know making radio is actually doing everything in one take to me, you know live mixing and those sorts of things. So I built my own radio studio, and I opened it last week in Amsterdam, custom built radio studio for A State of Trance which also includes visuals. So from this week on, every week, youll be able to watch ASOT on Facebook, Youtube and a bunch of other channels for free. I think thats the next level in radio, in visual radio and also I want to go more in detail with the specific tracks Im playing. So whether its an album special or just a new release with a big artist, Ill try to get that artist in the studio or have them Skype in and tell us something about the track so you know its next level for a radio show.

Armin: Very difficult. Its an unbelievably big undertaking. First of all, we rehearsed for three weeks in Holland in a very very big hall, because we built the entire stage just for rehearsing. So every individual track was rehearsed and thats what I learned from [my theater director]Jos Thie. He actually lives here, its very rare that you see him but hes a very famous TV director and knows nothing about dance music. He comes from the world of theater production. He did massive theater productions. And to get him involved, and again hes not a dance music fanatic, he doesnt know anything about dance music, but to have him look at my world and have him be like Okay so you want a singer right? You want a singer to perform? and I said Yeah.

And weve all seen those DJ sets where theres a vocalist that just awkwardly walks in, theres a mic thats plugged into the DJM-900 and then the singer starts singing, its like uhhhhh You know you dont really want that. I think what you should do with a live vocalist and this is what Jostold me, if you want to do a live performance or anything it almost needs to be better than the vocal coming from the record. I mean why would you do it live otherwise? It has to enhance your experience listening to the song, right? Otherwise you can just play the original track because it sounds better. Its already recorded so why the hell would you do it live? But I really learned that people do appreciate vocalists singing the song live and trying to interact with the crowd and its so much fun to do with that.

Armin: Well that was a big part of it. We really thought long and hard about how we were going to do the stage design because I was in the Forum with the Intense show and we had a completely different stage, but the idea behind the stage obviously is that the crowd embraces me. Because thats the catwalk so.

Armin: You know, this is also about learning from Jos. Shows like these are more about the individual moments rather than the entire set. So if Im in a club or at a festival, Ill play a set, a coherent set, so a set that tells a story. Ill build up BPM wise, key wise, Ill try to have some surprises obviously but theres some form of flow to your set which is what I try to do as a DJ. But with the Armin Only show I still try to keep that flow but its more like, if you play a crowd that big and you play for that long, its really hard to keep the attention from the crowd. Their attention span becomes really short.

Armin: Thank you and well I guess mission accomplished! It was always my dream of discovering this sort of uncharted territory like I said and I think Im not saying this show is perfect but I think that this is a way that dance music could be headed. Trying to keep it more exciting for the crowd than just playing the tracks. You know, really enhancing those particular moments for example with the intro or with Adagio, I mean that was a drone that goes on for 2 minutes and 10 seconds and theres a drone just laying there in D sharp that just goes and then theres a trumpet player going over that and I talked long and hard with Jos and with Sander about that and I was like Am I really gonna do this? Am I really gonna have a trumpet player in a dance music show for 2 minutes and 10 seconds and a ballet?

But everyone gets the story, thats the funny thing.

We kinda wrote it on the spot when we were rehearsing because that wasnt a thing. I just wrote a drone and I put a break in the track and I put the drone there and then Eric started playing this beautiful trumpet and when he did that the first time we were all like Wow this is a magical moment, and then we had this little story that we he starts playing Adagio he turns his back to the dancer and the dancer sees that he doesnt want her anymore and its kind of this love story and everybody gets it! People were like That was really moving, and its a very simply story but because its so small and so little the effect when the beat drops back its like BOOM! You know? Its because its so simple you know? So going from really small moments and really trying to find that emotion, I guess thats what we really all want from music right? And a normal DJ set doesnt really give me the opportunity you know? Like if youre playing at Ultra or EDC or any big festival its not really possible to just have a trumpet player playing on a drone for 2 minutes. *laughing*

Armin: Yeah! So this show gives me more creative freedom in that sense.

Armin: And what was happening I mean its really strange, I mean the track is called Adagio for Strings and we did it with a trumpet, its not called Adagio for Trumpet. *laughing*

Armin: When I started writing for this show, obviously its based on my last artist album Embrace and yeah, you think about How am I going to do a 4-5 hour show around this album? How am I going to keep that interesting? So I just started to write a lot of music, new music for this show and try to come up with different ideas. Half of the ideas didnt even make the show. Its a big Actually I dont even have an idea what the hell Im doing with this show. *laughing* But its just, Im just trying to do what I do in the studio. When Im in the studio Im just trying to make something that I want to listen to. Because you know what, when Im making music theres no people there. Its just maybe some guys Im working with but theres no crowd there. So you never know if what you think or hope would work actually works in reality. Thats also the reason why and why Im coming back to radio, why Im so happy I have my radio show because actually I think it gives me an advantage over the rest. If you do a radio show, you get to test your music, not only for your own ears but also thanks to social media people actually talk back. I find that magical.

Armin: So I get to use I get to use your ears! Like if you were a trance fan youd listen to the show, I get to use your ears. And no matter what you mean like we all have a right to say this song is shit or this song is great, but you know it happened a million times that I got a song song and I was so excited about it and Id go on air and say this is the new song by such and such, its so great, its my tune of the week and then the week after it doesnt even get 2% of all the votes, and the other way around as well. Sometimes I get a track, I listen to it and its by some big artist thats hot right then and Ill listen and be like Im not really digging this and I play it and the listeners go Oh my god this is so great! So when you read those comments you kind of start to understand that track better. Im not perfect, sometimes I really have to see those comments online and sort of interact with my fans.

Armin: Yeah. Every week still, to this day. Every week theres a track that I go I mean, its the most boring answer you can give to an answer but I say always the tracks on my radio show. Other DJs inspire me man, I mean now especially, now that Im forty, I get so inspired by twenty one and twenty two year olds. Last year in Miami every single DJ was inspiring me. I went to Martin Garrix, I went to Oliver Heldens, I went Armand Van Helden, and all these DJs have such different approaches and I find it so exciting and so refreshing. I think that at the moment you have no reason to be negative about dance music, even though some of it is commercial. I think even though we have this bit of dance music on the radio and its commercial, there also is this massive underground that is flourishing and its so great. I mean twenty years ago it was completely unthinkable to have a festival with multiple genres and now its like you can listen to bass or drum n bass or whatever you want. Its a festival for you and I think that its phenomenal! I mean theres not a lot of dance music thats out there that I dont like.

Armin: Exactly! Thats my point, especially within the trance scene right now there seems to be a group of people that are very negative and they claim they can say what is trance and what is not and its basically if its not what they like then its not trance. Im like excuse me, are you the one that decides that? Also, trance is so broad, it used to be this and now you have the Arty stuff, the Above and Beyond stuff, which is fantastic. Then you have the Dash Berlin sort of EDM-pop kind of thing which is also phenomenal I think. Then you have Simon Patterson and Bryan Kearney, the more like tech-y psytrance kind of stuff, then you have the uplifting stuff that they call Orchestrance with the massive breakdowns and all the emo stuff, which is also fantastic. I want ASOT to be the home for all these sub-genres. Im not trying to exclude somebody or say this is not trance and this is not trance. If you think that Im not trance, fine, then Im not for you. Great, youre the customer so youre always right. But for me, everything I play is trance or dance music or whatever so I dont really want to I never look at Beatport at just in the trance genre, I also look at techno. Sometimes theres an amazing Remember that rack Oxia Domino or something which is also on Compact? Which is another techno label. Its almost a classic trance record when it was labelled techno. Thats what I mean though, music gets exciting if you cross that border and try to get out of your safety zone and open up a little bit.

Armin: Oh wow thats very cool! Yeah Gareth has always been very much on the forefront. I know Roxanne and Gareth well, and Ive always been a massive supporter of Gareth, hes done a lot of good for the scene. To be honest, I think of Saving Light, I even played it at the Forum, I didnt expect it to be number one but I think its great for trance. But you know I find sometimes, Beatport is not necessarily representative for whats happening on the dance floor. Maybe Im silly, but sometimes I feel theres no charts out there that are representing the dance floor adequately. I look at the Beatport top 100 and if I play any of the top 30 tracks my crowd would leave. Not to say its good or bad music but you know, I feel that the top 20 or 30 is not one hundred percent representative. It does mean something of course.

Armin: Yeah, 1001tracklists is important, I find that more representative of whats going on on the dance floor than I do with Beatport. But then again, having said that I love Beatport for its interface, for the fact that I can get WAV files there. I hope it will never die, because Beatport is amazing and it saved my life so many times. *laughing* You know, really I think its an amazing website and I think what those guys are doing is phenomenal. I just wish that we had a chart that was more representative of whats actually happening on dance floors.

Armin: Yeah but I mean it does say something. I know DJ Mag gets so much shit, and to a certain extent I can agree with the comments, but having said that I think what is good about any award show, charts, DJ Mag, whatever; what is good about it is the fact that we talk about it. That debate is needed in the scene very much, because even though you may not agree with the results of DJ Mag, I think that if you see a DJ that comes into the DJ Mag top 10 that you dont know, he must do something right. Yes, there is a lot of cheating going on, but you cannot cheat your way into the top 10, theres no way. There must be some truth to it. Maybe the numbers are not correct but it does say something. I do think its good for dance music because it does point a lot of attention to new talent that is coming up and it does really say something about the scene, whether the charts correct or not.

Armin: Yeah for me its a whole revival of the 92, 93 sound, and hes a phenomenal producer and amazing guy.

Armin: But yeah, it feels like that whole sound is coming back again, its just amazing. I think everything that is happening is amazing in that sense.

Armin: You know what, heres a bold statement. I dont really think that you can speak of dance music anymore. Because if you look at the top 40 right now, almost every track is produced in a dance music way. So, my statement is that electronic music has spread like an oil stain through all genres of music almost.

Most music is produced in a dance music way or a sequence. Most kicks even in rock records now are processed. You know, I find it very difficult to state. Im very interested in this, I like to read a lot of biographies, and if you look at for example lets take the Beatles. They started to involve Moog synthesizers on Sgt. Peppers Lonely Hearts Club Band and this was a very important moment in time. Why? Because at that point in 1964 the synthesizer was regarded as a devil-ish instrument. You know it wasnt an instrument because it was electronic and it wasnt real, it was not a real instrument so the fact that the Beatles had the guts to incorporate a Moog synthesizer on Sgt. Peppers Lonely Hearts Club Band was at that point in time a very essential point.

Heres where Im going: fast forward to this time and what theyre basically doing is theyre trying to involve unconventional instruments into their sound. This has always been essential for any music development, so my answer to your question is what does the future for dance music hold? I think its mixing and merging. I mean bass or trap music comes from, you know I hear elements, clear elements from jungle and two step. You know the two step sound from the late 90s, early 2000s in the UK. Speed garage, you remember that sound? So all these production elements, all these new sounds that are coming, theres these wiz kids that make the most amazing plug-ins that make the most amazing sound. Ableton is made for the birth of dance music.

So what you see is that technology has always had a massive, MASSIVE, influence on the development of sound.

Take Skrillex for example, without Ableton there would be no Skrillex, or the sound would be very different at least. Ableton has just opened so many doors for so many producers which is phenomenal, I think. So, my point is that I think in the future dance music will be mixing and merging and the development of new techniques has a massive role in that. So if you look in the KVR audio, the website, you see all these new plug-ins that are coming and they will have an effect on the sound of the future. And what I find exciting about trance music is if you look at I dont know many about other genres or styles but what I find exciting about trance music is that you can really see these eras in trance.

For example, in 2007 minimal became really big, and you saw that the trance producers were trying to incorporate the minimal sound into their tracks. Right now, whats really a trend in dance music is that a lot of DJs are trying to involve the impact of a psytrance produced track into trance. So a lot of the psytrance tracks, a lot of the trance producers look at psytrance because psytrance is so minimalistically produced compared to uplifting trance that the impact is a lot bigger because the kicks are not that long, you know sometimes in a trance track the bassline and kick are really fighting especially in a big room and then theres 3 notes of bassline so what you hear in a big room is this one big noise but in psytrance all the bass noises are a lot shorter, its like really short, so you can really hear right now in trance and a lot of trance uplifting producers try to copy that impact that a psytrance track has and I think thats amazing. Theres a development happening right here and right now. So thats just an example of what I think will happen in the future, theres just going to be a lot of mixing and merging.

Armin: Well Im super excited for the big show that Im doing on the 12th and 13th of May, Im finally doing the Amsterdam Arena which is a stadium and Im super excited about that. I hope people will appreciate the new radio show formula that Ive got going on and I hope you guys will let me know and tune in every weekend, and even if you dont have time to tune in live you can always go to Facebook or YouTube and watch the episode. Ive got a lot of new stuff coming up and Im excited and I hope to see you guys somewhere. Also, thank you for all the support weve had. Guys like you are becoming more and more important, its great. You guys are really influencing the scene which is very good I think.

All images viaAlive Coverage, Marc van de Aa

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What Happens When A Bass Head Interviews Armin Van Buuren About Trance [Exclusive] – Your EDM

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Ionis Earns $75M Milestone from Bayer for Progress of Antisense Drug Program – Genetic Engineering & Biotechnology News (press release)

Posted: at 11:12 am

Ionis Pharmaceuticals will receive a $75 development milestone payment from Bayer, relating to the continued clinical development of the antisense drug IONIS-FXIRx and the start of a clinical program for a second antisense candidate, IONIS-FXI-LRx. Ionis says it plans to start a Phase IIb study with IONIS-FXIRX in patients with end-stage renal disease who are on hemodialysis. “We recently completed a Phase II study in patients with end-stage renal disease on hemodialysis, in which IONIS-FXIRxdemonstrated robust reductions in Factor XI activity and no treatment-related major bleeding, ” stated B. Lynne Parshall, COO at Ionis Pharmaceuticals. The firm will also take IONIS-FXI-LRx through Phase I development. Under terms of the agreement with Bayer, once these studies have been carried out, and if Bayer decides to progress the programs, the German drugs giant will take over responsibility for all global development, regulatory, and commercialization activities for both drugs. Ionis will be eligible for development milestones, plus tiered royalties up to the high 20% range, on gross margins of both drugs combined.

IONIS-FXIRx and IONIS-FXI-LRx are antisense drugs designed to reduce the production of Factor XI. IONIS-FXI-LRx has been developed using Ioniss Ligand Conjugated Antisense (LICA) platform. We are pleased that Bayer has decided to expand our collaboration and initiate development of a LICA antisense drug targeting Factor XI,” Parshall added. “Our LICA technology enables flexible, low, and infrequent doses and dose regimens, which may be preferred for a drug targeting broad indications.”

Earlier this month Ionis earned a $5 million milestone payment from partner Biogen following the validation of a neurological disease target. Biogen and Ionis have a broad drug development collaboration in the field of neurological disorders. In December 2016, the FDA approved the firms’ antisense drug SpinrazaTM for the treatment of spinal muscular atrophy in pediatric and adult patients. During January of this year, Novartis agreed to a potentially $1B global option and collaboration agreement to develop the Ionis subsidiary Akcea Therapeutics’s cardiovascular disease candidates AKCEA-APO(a)-LRx and AKCEA-APOCIII-LRx.

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Ionis Earns $75M Milestone from Bayer for Progress of Antisense Drug Program – Genetic Engineering & Biotechnology News (press release)

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ApolloBio Licenses Inovio’s Late-Stage HPV DNA Immunotherapeutic for China – Genetic Engineering & Biotechnology News

Posted: February 13, 2017 at 8:50 am

Chinese biomed ApolloBio negotiated exclusive rights to develop Inovios lead DNA immunotherapeutic for human papillomavirus (HPV), VGX-3100, within China, Hong Kong, Macao and Taiwan. The collaboration and licence agreement covers development of VGX-3100 for treating and/or preventing pre-cancerous HPV infections and HPV-driven dysplasias, and excludes HPV-driven cancers and all combinations of VGX-3100 with other immunostimulants.

Under terms of the deal ApolloBio will fund all clinical development costs for VGX-3100 within its licensed geographies. Inovio will earn $15 million in up front and near term payments, and could receive another $20 million in regulatory milestones, plus double digit sales royalties.

$12 million of near-term payments will be made to Inovio when FDA lifts the existing VGX-3100 Phase III pre-initiation clinical hold, which has been in place since October 2016. The agency refused to allow the start of the proposed Phase III VGX-3100 trial because it wanted additional data on the shelf-life of disposable parts of the CELLECTRA 5PSP immunotherapy delivery device.

ApolloBio has separately agreed to invest up to $35 million in Inovio, after the clinical hold has been lifted. The firms said that the aggregate investment may be kept below an amount that would make ApolloBio the largest shareholder in Inovio.

The firms claim that there are currently no approved non-surgical treatments for persistent HPV infection or cervical dysplasia. Commenting on the deal with Inovio, Dr. Weiping Yang, ApolloBios CEO, said, We are delighted to begin 2017 with a strategic collaboration with Inovio. VGX-3100 is the worlds first therapeutic vaccine being developed for HPV pre-cancers. This collaboration, license and equity investment marks our determination to introduce late stage innovative new drugs to meet severely unmet medical needs within the Greater China region.

Inovio is exploiting its SynCon DNA plasmid technology and electroporation delivery platform to develop DNA immunotherapeutics against multiple cancers and infectious diseases, including HIV and hepatitis. VGX-3100 is designed to activate functional, antigen-specific CD8 T-cells to clear persistent HPV 16/18 infection, and to reverse the development of precancerous cervical dysplasia.

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ApolloBio Licenses Inovio’s Late-Stage HPV DNA Immunotherapeutic for China – Genetic Engineering & Biotechnology News

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DACC & Virgin Galactic team up to explore virtual reality – Las Cruces Sun-News

Posted: February 11, 2017 at 8:29 am

Sun-News Reports , . 10:29 p.m. MT Feb. 10, 2017

Dona Ana Community College virtual reality faculty and students unpack virtual reality equipment from VR First(Photo: Stephen Osborne)

LAS CRUCES This spring DACC students will take a space flight and learn aerospace fundamentals in a Virtual Reality environment.

Doa Ana Community College and Aerospace innovators Virgin Galactic have announced an exciting collaborative education and outreach research project. The core idea will be to work and learn together, exploring the newest technologies and possible uses of VR in research, education, business, and career technical education.

Our students and instructors are pleased and honored to work with Virgin Galactic on this exciting initiative, said Matt Byrnes, DACC Creative Media Technology Director. Thanks to Dr. Kevin Boberg, Vice President of Economic Development for the New Mexico State University Arrowhead Business and Research Park and Wayne Savage of Arrowhead Center for helping this collaboration take place.

The program will start with a VR simulation that explains core concepts of aerospace fundamentals and gives students, particularly at the Las Cruces Public Schools Challenger Center, an immersive virtual spaceflight experience.

Many people are familiar with the term virtual reality but are unsure about the uses of this technology, Byrnes said. Gaming is an obvious virtual reality application, but there are many different uses, some you might expect and others not so much.

Irrespective of the use, virtual reality produces a set of data which could then be used to develop new models, training methods, communication and interaction, said Mark Butler of Virgin Galactic. In many ways the possibilities are endless.

In September, 2016 DACC became one of only 24 VR First partner institutions worldwide, sponsored by the German game engine development firm Crytek and was awarded several thousand dollars of the newest hardware and software giving DACC students access to the latest VR development tools.

According to Byrnes, This kind of collaboration between the private and public sectors and between technology companies and the creative media arts is central to efforts to develop the larger Creative Campus efforts at Arrowhead Park and build a larger toolset to positively impact not only Aerospace but Healthcare, Agricultural Technology and other industries growing in our community.

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DACC & Virgin Galactic team up to explore virtual reality – Las Cruces Sun-News

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Little Libertarians on the Prairie: The Hidden Politics Behind a … – History

Posted: February 7, 2017 at 10:53 pm

Laura Ingalls Wilder as a schoolteacher, c. 1887. (Credit: Fine Art Images/Heritage Images/Getty Images)

Born on the American frontier on February 7, 1867, Laura Ingalls Wilder turned her memories of being a pioneer girl into the Little House on the Prairie books, one of the most popular childrens series of all time. Unknown to many, however, is that Wilder didnt write the books alone. On the 150th anniversary of Wilders birth, learn about her secret collaborator on the Little House on the Prairie books and her little-known connection to the Libertarian Party.

Laura Ingalls Wilder wasnt your typical debut novelist when her first book, Little House in the Big Woods, was published in 1932. She was 65 years old, decades removed from the childhood memories that provided the foundation for her colorful story of hardship, adventure and survival on the Wisconsin frontier that struck a chord in Depression-era America.

Children devoured the wholesome tales celebrating family, self-reliance, hard work and neighbor helping neighbor. There had never been anything like this for children, telling them what the pioneer daysa time in history that was still pretty recentwere like, says Christine Woodside, author of the new book Libertarians on the Prairie: Laura Ingalls Wilder, Rose Wilder Lane, and the Making of the Little House Books.

Wilder authored seven more books over the next 11 years, including Little House on the Prairie, which chronicled the exploits of the itinerant Ingalls family as they endured everything from blizzards of grasshoppers to plagues of snow as they rattled westward in their covered wagon across the wilderness and plains of the upper Midwest in the late 1800s before finally settling in the Dakota Territory.

While only the name of Laura Ingalls Wilder was emblazoned on the book covers of one of the most popular series in American literary history, scholars researching her family papers slowly came to the conclusion in the decades following her 1957 death that the beloved stories of Pa, Ma and sisters Mary, Carrie and Grace were the product of not just one womanbut two.

Unknown to readers at the time, Wilder secretly received considerable assistance from her only adult child, Rose Wilder Lane. While Wilder was an unknown author when Little House in the Big Woods was published, Lane was one of the most famous female writers in the United States, having penned novels, biographies of Charlie Chaplin and Herbert Hoover and short stories for magazines such as Harpers, Cosmopolitan and Ladies Home Journal.

Unlike her mother, however, Lane had little affinity for the hardscrabble life of the American heartland and left the familys Missouri farm as a teenager, eventually moving to San Francisco. Able to speak five languages, she traveled extensively and by the 1920s was living in Albania in a large house staffed by servants. Although she always had a tense relationship with her mother, Lane began to long for home and returned to the family farm in 1928.

Knowing a good story when she heard one, Lane prodded her mother to put her childhood experiences to paper. Wilder, however, had little literary experience outside of pieces that she wrote for rural newspapers. Lane, though, knew how to make a manuscript sing and hold chapters together, and she used her contacts in the publishing industry to sell Little House in the Big Woods.

Laura had lived the life. She had the memory. However, she didnt have any experience making a novel, Woodside tells HISTORY. Rose knew how to do that. They were each crucial to the book. Laura couldnt have written the books without Rose, and Rose couldnt have written them without Laura.

Lane not only polished her mothers prose but infused Wilders stoic outlook with the joy and optimism that connected with many readers. The authors secret collaborator also sanitized Wilders real-life experiences for an audience of children, scrubbing away the hard edges such as the death of a baby brother at 9 months of age and replacing stories of murders on the frontier with images of swimming holes and bonneted girls in dresses skipping through tall grasses and wildflowers.

Woodsides book also shines light on the political views of Wilder and her secret collaborator that were below the surface of the Little House series. Like many Americans, the Wilders were hit hard by the Great Depression. Both mother and daughter were dismayed with President Franklin D. Roosevelts New Deal and what they saw as Americans increasing dependence on the federal government. A life-long Democrat, Wilder grew disenchanted with her party and resented government agents who came to farms like hers and grilled farmers about the amount of acres they were planting.

They both hated the New Deal, Woodside says of Wilder and Lane. They thought the government was interfering in peoples lives, that individuals during the Depression were becoming very whiny and werent grabbing hold of their courage. The climate of America was really irritating them. The New Deal, for a lot of farmers and definitely the Wilders, made them change their politics.

An acquaintance of Ayn Rand and a critic of Keynesian economics, Lane would become an early theorist of the fledgling political movement that would eventually form the Libertarian Party in 1971. Neither woman set out to indoctrinate children with their political views, but their beliefs in individual freedom, free markets and limited government can be seen in the pages of the Little House books. Lane didnt explicitly use it as a political manifesto, Woodside says. She was being who she was, and they both felt strongly that the pioneers should be examples to people. It was inevitable she was going to flesh out the story by focusing things like free-market forces at work in the general store and farmers being free and independent.

While the Little House books emphasized self-reliance, at least two instances of government assistance that benefited the Ingalls family were downplayed. In addition to receiving their land in the Dakota Territory through the Homestead Act, it was the Dakota Territory that paid for the tuition of Mary Ingalls at the Iowa School for the Blind for seven years. Its an inconvenient fact, Woodside says. Rose suppressed that detail.

Ultimately, close quarters and close collaboration caused the fault lines between mother and daughter to reappear. The pair became estranged, and Lane moved to Connecticut, where in 1943 she wrote The Discovery of Freedom: Mans Struggle Against Authority, considered to be a libertarian manifesto. By World War II, Lane refused a ration card, grew and canned most of her food and deliberately curtailed her writing in order to pay as little tax as possible.

After inheriting the royalty rights to the Little House series after Wilders death in 1957, Lane donated money to the Freedom School in Colorado, a free-market academy that taught libertarian theory. When she died suddenly in 1968, future Little House royalties were bequeathed to her sole heir and political disciple, lawyer Roger Lea MacBride. In addition to becoming the first person to cast an electoral vote for a Libertarian Party ticket in 1972, MacBride was the Libertarian Party candidate for president four years later.

Both mother and daughter carried the secret of their collaboration to their graves. By the time a new generation of children were becoming exposed to Wilders stories through the Little House on the Prairie television show, on which MacBride served as a co-creator and co-producer, scholars were learning of the partnership from the womens letters and diaries. Laura and Rose were very clearly collaborators from day one on these books, Woodside says. Our understanding and celebrating that is essential to understanding why these books are so wonderful.

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Artificial Intelligence – Graduate Schools of Science …

Posted: November 23, 2016 at 10:00 pm

Artificial Intelligence (AI) is a field that develops intelligent algorithms and machines. Examples include: self-driving cars, smart cameras, surveillance systems, robotic manufacturing, machine translations, internet searches, and product recommendations. Modern AI often involves self-learning systems that are trained on massive amounts of data (“Big Data”), and/or interacting intelligent agents that perform distributed reasoning and computation. AI connects sensors with algorithms and human-computer interfaces, and extends itself into large networks of devices. AI has found numerous applications in industry, government and society, and is one of the driving forces of today’s economy.

The Master’s programme in Amsterdam has a technical approach towards AI research. It is a joint programme of the University of Amsterdam and VrijeUniversiteit Amsterdam. This collaboration guarantees a wide range of topics, all taught by world renownedresearchers who are experts in their field.

In this Master’s programme we offer a comprehensive collection of courses. It includes:

Next to the general AI programme we offer specialisations in:

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Artificial Intelligence – Graduate Schools of Science …

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Trance (2013 film) – Wikipedia, the free encyclopedia

Posted: September 10, 2016 at 5:30 am

Trance is a 2013 British psychological thriller film directed by Danny Boyle with a screenplay by Joe Ahearne and John Hodge from a story by Ahearne. The film stars James McAvoy, Vincent Cassel, and Rosario Dawson. The world premiere of the film was held in London on 19 March 2013.

Simon (McAvoy), an art auctioneer, becomes an accessory to the theft of a paintingGoya’s Witches in the Airfrom his own auction house. When a gang attacks during an auction, Simon follows the house emergency protocol by packaging the painting. The gang’s leader Franck (Cassel) then takes the package from him at gunpoint. Simon attacks Franck, who delivers him a blow to the head that leaves him with amnesia. When Franck gets home, he discovers that the package contains only an empty frame. After ransacking Simon’s apartment and trashing his car, the gang kidnaps and unsuccessfully tortures him. But he has no memory of where he has hidden the painting. Franck decides to hire a hypnotherapist to try to help him remember.

Franck makes Simon choose a hypnotist from a directory, and he chooses a woman named Elizabeth Lamb (Dawson). As a first hypnotic exercise, Simon recalls where he put some car keys. Elizabeth exposes the gang’s plan to have her hypnotize him, and demands partnership. In a next hypnotic episode, under gang supervision, Simon remembers that, shortly after the blow to his head, he awoke alone. On finding the stolen painting hidden in his suit, he left the art gallery. Distracted by a phone text message, while crossing the road, he was hit by a red car. The female driver tried to take him to hospital. Simon, in a kind of memory fugue, believed the woman was Elizabeth, recalling that she had made him forget her. The gang tries violently to force Simon to remember where he put the painting, and that ends the hypnotic episode. When asked by Elizabeth about how he met Franck, Simon confesses that he has a gambling problem. Franck helped him pay his debts in exchange for his help in stealing the painting.

To help Simon recover from the violence, Elizabeth stays overnight in his apartment. In the morning, Simon dreams of Elizabeth’s having used a brain-scan behaviour-conditioning technique to erase an obsession that he had for her. Elizabeth tells Franck about that.

For the next step to recover the painting, Elizabeth tells Franck that she will sexually seduce Simon. Simon’s feelings for Elizabeth recur, gently this time. At the same time, Franck and Elizabeth have unanticipated sex, and she steals his pistol from his bedside drawer. Nate, a gang member, sees them together and warns Simon, who confronts Elizabeth with it. She responds by touching his erotic mindspot, related to Goya’s Nude Maja.

Remembering where the painting is, Simon goes with Franck and his associates to reveal the location, but overhears their plan to kill him. He calls Elizabeth and tells her that the painting is in a red car in a certain car park, and unable to leave Franck’s apartment, he returns and kills the gang members instead. After shooting Franck, Simon wakes up: this was all dream, and he still is in Elizabeth’s apartment. Elizabeth takes the car keys and goes to get the painting, leaving Franck’s pistol for Simon. While searching for Elizabeth, Simon finds Franck in her apartment. Nate and his associates intercept Elizabeth and bring her there. Franck takes Simon to get the painting, and as he kisses Elizabeth, she secretly passes three bullets into his mouth. On the elevator, Simon stops Franck with a fire extinguisher, and then with the pistol, now loaded with the three bullets. In the apartment, Nate prepares to rape Elizabeth. Heading back into the apartment, Simon shoots the three gang members. He takes the car keys from Elizabeth, and loads the pistol with the remaining three bullets. He takes Elizabeth to get the painting, and she tells him to let Franck come with them. With Franck driving, Simon leads them to a parking garage where the painting is.

They collect the red car and drive it to a safe warehouse. During the trip, Elizabeth reveals that Simon was previously a client of hers. He had a gambling addiction he wanted to fix. They started an affair, and she found his erotic mindspot. However, he became obsessed with her, and eventually abusive. Fearing for her life, she re-directed the hypnosis to make him forget her. This led him back into his gambling addiction, which as previously stated, caused him to go in debt and to try to pay it off by stealing a painting, with the help of Franck. Simon recalls that, after the heist, when he was hit by the red car and mistook the female driver for Elizabeth, he strangled her.

At the warehouse, in the car’s trunk, Elizabeth finds the painting and the body of the female driver. Simon, having at last remembered his past and wanting to forget, douses the car in fuel with Franck zip-tied to the steering wheel, sets it on fire and tells Elizabeth to run away with the painting. She runs away but promptly returns driving a truck which she drives into Simon, pinning him against the other car, and ultimately sending Simon, and the car Franck is trapped in, into the river.

Franck manages to escape, while it is implied that Simon is killed. The scene cuts to Franck swimming in his apartment while thinking of the event. He gets out of the pool and receives a package. He opens the package and finds an iPad that plays a video of Elizabeth talking about the painting, which is now hanging in her apartment. She reveals that when she hypnotized Simon to make him forget her, she also hypnotized him to go back into his gambling addiction and then try to steal a painting to pay off his debt. When this happened, he would instead give the painting over to Elizabeth. This explains why Simon took the painting away from Franck at the beginning and the text message he received before being hit by the car, which is revealed to be from Elizabeth telling Simon to deliver the painting to her. Elizabeth tells Franck that he can search for her and try to find her, but also gives him the option to forget the entire ordeal, and a button for an app called “Trance” appears as the video ends. Franck is shown debating whether to press the button just as the screen cuts to black.

After director Danny Boyle filmed Shallow Grave in 1994, Joe Ahearne sent the director his screenplay for Trance, seeking Boyle’s encouragement. Boyle thought that the project would be “quite difficult” for a beginning screenwriter. Ahearne later turned the script into a 2001 television movie.[5][6] Boyle never forgot it, and almost two decades after their original conversation he contacted Ahearne about turning it into a feature film.[7] Partially based on Ahearne’s 2001 British television film of the same name, Trance underwent script doctoring by screenwriter John Hodge marking the fifth motion picture collaboration between Hodge and Boyle.[8]

In May 2011, Michael Fassbender was cast as Franck but dropped out due to scheduling conflicts.[9][10]Colin Firth was considered for the part before Cassel was cast.[11][12]Scarlett Johansson, Melanie Thierry, and Zoe Saldana were considered for the role that went to Dawson.[12][13]

McAvoy, who accepted the role in 2011, said that he almost turned down the part, while reading the script, because Simon seemed to be a victim, which didn’t interest him. He told NPR’s reporter Laura Sullivan, “And then I got about 15 or 20 pages in, and I started to sense that something else was coming in the character. And then something else did come. And then about every 10 pages, something else came. Until at the end, I was hunching at the bit, as we say in Scotland… It just means I was desperate…I was hungry to play this part.”[14]

Principal photography began in September 2011. After filming wrapped up, the film was placed on hold in order for Boyle to work on the opening ceremony of the 2012 Summer Olympics in London. Post-production was then picked up again in August 2012.[15]

Boyle said that this is “the first time I put a woman at the heart of a movie.”[8] He also said that he originally intended to set the movie in New York City,[16] but it was filmed in London and in Kent instead, as Boyle’s Olympic ceremony duties meant he had to stay in the UK.[17]

On 4 January 2013, it was announced that Rick Smith of the band Underworld would be composing the music for the film.[18] Underworld previously contributed tracks to other Danny Boyle films, including Trainspotting (1996), A Life Less Ordinary (1997), The Beach (2000), and Sunshine (2007). About the collaboration, Smith said, “After finishing the Opening Ceremony, I hardly knew what day of the week it was. I took a month off work, off music, off everything. Exactly one month and three days after we said goodbye in the stadium, I received a text from Danny that said, ‘Do you ever want to hear from me again workwise and would that go as far as having a chat about Trance… Questions, questions.’ Two Minutes later I was on board.”[19] The soundtrack album for Trance was released in the United Kingdom on 25 March and in the United States on 2 April 2013.[19][20]

When asked by an interviewer about the secret of their 17-year-old creative partnership, Boyle joked, “He’s cheap.” Then, answering seriously, he said that they both like electronic music and that he doesn’t prescribe a sound for a scene, but lets Smith follow his own instincts.[21]

Boyle showed a teaser trailer and an extended version of an alternate ending at South by Southwest on 9 March 2013.[22][23] The entire film could not be screened at the festival, as is usually done, because the producing studio Path owned the rights to the world premire.[24] The world premire of the film was held in London on 19 March 2013.[25] The film saw general release on 27 March 2013 in the United Kingdom,[26] with a United States release date on 5 April 2013.[27]

The film received mostly positive reviews from critics. Rotten Tomatoes gives a score of 68% based on reviews from 160 critics; the site’s consensus is: “As stylish as ever, director Danny Boyle seems to be treading water with the surprisingly thinly written Trance — but for fans of Boyle’s work, it should still prove a trippily entertaining distraction”.[28]Washington Post writer Michael O’Sullivan describes Boyle as “playing fast and loose with reality.”[29]

On Metacritic, which assigns a weighted mean rating out of 100 based on reviews from film critics, the film has a rating score of 61% based on 37 reviews.[30]

Empire magazine in its review gave the film 4 out of 5 and called the film “a dazzling, absorbing entertainment which shows off Danny Boyle’s mastery of complex storytelling and black, black humour.”[31]Empire also ranked it 27 in its top 50 films of 2013.[32]

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History of artificial intelligence – Wikipedia, the free …

Posted: August 30, 2016 at 11:03 pm

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with “an ancient wish to forge the gods.”

The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

The Turing test was proposed by British mathematician Alan Turing in his 1950 paper Computing Machinery and Intelligence, which opens with the words: “I propose to consider the question, ‘Can machines think?'” The term ‘Artificial Intelligence’ was created at a conference held at Dartmouth College in 1956.[2]Allen Newell, J. C. Shaw, and Herbert A. Simon pioneered the newly created artificial intelligence field with the Logic Theory Machine (1956), and the General Problem Solver in 1957.[3] In 1958, John McCarthy and Marvin Minsky started the MIT Artificial Intelligence lab with $50,000.[4] John McCarthy also created LISP in the summer of 1958, a programming language still important in artificial intelligence research.[5]

In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again.

McCorduck (2004) writes “artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized,” expressed in humanity’s myths, legends, stories, speculation and clockwork automatons.

Mechanical men and artificial beings appear in Greek myths, such as the golden robots of Hephaestus and Pygmalion’s Galatea.[7] In the Middle Ages, there were rumors of secret mystical or alchemical means of placing mind into matter, such as Jbir ibn Hayyn’s Takwin, Paracelsus’ homunculus and Rabbi Judah Loew’s Golem.[8] By the 19th century, ideas about artificial men and thinking machines were developed in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots), and speculation, such as Samuel Butler’s “Darwin among the Machines.” AI has continued to be an important element of science fiction into the present.

Realistic humanoid automatons were built by craftsman from every civilization, including Yan Shi,[11]Hero of Alexandria,[12]Al-Jazari and Wolfgang von Kempelen.[14] The oldest known automatons were the sacred statues of ancient Egypt and Greece. The faithful believed that craftsman had imbued these figures with very real minds, capable of wisdom and emotionHermes Trismegistus wrote that “by discovering the true nature of the gods, man has been able to reproduce it.”[15][16]

Artificial intelligence is based on the assumption that the process of human thought can be mechanized. The study of mechanicalor “formal”reasoning has a long history. Chinese, Indian and Greek philosophers all developed structured methods of formal deduction in the first millennium BCE. Their ideas were developed over the centuries by philosophers such as Aristotle (who gave a formal analysis of the syllogism), Euclid (whose Elements was a model of formal reasoning), Muslim mathematician al-Khwrizm (who developed algebra and gave his name to “algorithm”) and European scholastic philosophers such as William of Ockham and Duns Scotus.[17]

Majorcan philosopher Ramon Llull (12321315) developed several logical machines devoted to the production of knowledge by logical means;[18] Llull described his machines as mechanical entities that could combine basic and undeniable truths by simple logical operations, produced by the machine by mechanical meanings, in such ways as to produce all the possible knowledge.[19] Llull’s work had a great influence on Gottfried Leibniz, who redeveloped his ideas.[20]

In the 17th century, Leibniz, Thomas Hobbes and Ren Descartes explored the possibility that all rational thought could be made as systematic as algebra or geometry.[21]Hobbes famously wrote in Leviathan: “reason is nothing but reckoning”.[22]Leibniz envisioned a universal language of reasoning (his characteristica universalis) which would reduce argumentation to calculation, so that “there would be no more need of disputation between two philosophers than between two accountants. For it would suffice to take their pencils in hand, down to their slates, and to say each other (with a friend as witness, if they liked): Let us calculate.”[23] These philosophers had begun to articulate the physical symbol system hypothesis that would become the guiding faith of AI research.

In the 20th century, the study of mathematical logic provided the essential breakthrough that made artificial intelligence seem plausible. The foundations had been set by such works as Boole’s The Laws of Thought and Frege’s Begriffsschrift. Building on Frege’s system, Russell and Whitehead presented a formal treatment of the foundations of mathematics in their masterpiece, the Principia Mathematica in 1913. Inspired by Russell’s success, David Hilbert challenged mathematicians of the 1920s and 30s to answer this fundamental question: “can all of mathematical reasoning be formalized?”[17] His question was answered by Gdel’s incompleteness proof, Turing’s machine and Church’s Lambda calculus.[17][24] Their answer was surprising in two ways.

First, they proved that there were, in fact, limits to what mathematical logic could accomplish. But second (and more important for AI) their work suggested that, within these limits, any form of mathematical reasoning could be mechanized. The Church-Turing thesis implied that a mechanical device, shuffling symbols as simple as 0 and 1, could imitate any conceivable process of mathematical deduction. The key insight was the Turing machinea simple theoretical construct that captured the essence of abstract symbol manipulation. This invention would inspire a handful of scientists to begin discussing the possibility of thinking machines.[17][26]

Calculating machines were built in antiquity and improved throughout history by many mathematicians, including (once again) philosopher Gottfried Leibniz. In the early 19th century, Charles Babbage designed a programmable computer (the Analytical Engine), although it was never built. Ada Lovelace speculated that the machine “might compose elaborate and scientific pieces of music of any degree of complexity or extent”.[27] (She is often credited as the first programmer because of a set of notes she wrote that completely detail a method for calculating Bernoulli numbers with the Engine.)

The first modern computers were the massive code breaking machines of the Second World War (such as Z3, ENIAC and Colossus). The latter two of these machines were based on the theoretical foundation laid by Alan Turing[28] and developed by John von Neumann.[29]

In the 1940s and 50s, a handful of scientists from a variety of fields (mathematics, psychology, engineering, economics and political science) began to discuss the possibility of creating an artificial brain. The field of artificial intelligence research was founded as an academic discipline in 1956.

The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 30s, 40s and early 50s. Recent research in neurology had shown that the brain was an electrical network of neurons that fired in all-or-nothing pulses. Norbert Wiener’s cybernetics described control and stability in electrical networks. Claude Shannon’s information theory described digital signals (i.e., all-or-nothing signals). Alan Turing’s theory of computation showed that any form of computation could be described digitally. The close relationship between these ideas suggested that it might be possible to construct an electronic brain.[30]

Examples of work in this vein includes robots such as W. Grey Walter’s turtles and the Johns Hopkins Beast. These machines did not use computers, digital electronics or symbolic reasoning; they were controlled entirely by analog circuitry.[31]

Walter Pitts and Warren McCulloch analyzed networks of idealized artificial neurons and showed how they might perform simple logical functions. They were the first to describe what later researchers would call a neural network.[32] One of the students inspired by Pitts and McCulloch was a young Marvin Minsky, then a 24-year-old graduate student. In 1951 (with Dean Edmonds) he built the first neural net machine, the SNARC.[33]Minsky was to become one of the most important leaders and innovators in AI for the next 50 years.

In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think.[34] He noted that “thinking” is difficult to define and devised his famous Turing Test. If a machine could carry on a conversation (over a teleprinter) that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was “thinking”. This simplified version of the problem allowed Turing to argue convincingly that a “thinking machine” was at least plausible and the paper answered all the most common objections to the proposition.[35] The Turing Test was the first serious proposal in the philosophy of artificial intelligence.

In 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess.[36]Arthur Samuel’s checkers program, developed in the middle 50s and early 60s, eventually achieved sufficient skill to challenge a respectable amateur.[37]Game AI would continue to be used as a measure of progress in AI throughout its history.

When access to digital computers became possible in the middle fifties, a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought. This was a new approach to creating thinking machines.[38]

In 1955, Allen Newell and (future Nobel Laureate) Herbert A. Simon created the “Logic Theorist” (with help from J. C. Shaw). The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead’s Principia Mathematica, and find new and more elegant proofs for some.[39] Simon said that they had “solved the venerable mind/body problem, explaining how a system composed of matter can have the properties of mind.”[40] (This was an early statement of the philosophical position John Searle would later call “Strong AI”: that machines can contain minds just as human bodies do.)[41]

The Dartmouth Conference of 1956[42] was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference included this assertion: “every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it”.[43] The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research.[44] At the conference Newell and Simon debuted the “Logic Theorist” and McCarthy persuaded the attendees to accept “Artificial Intelligence” as the name of the field.[45] The 1956 Dartmouth conference was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI.[46]

The years after the Dartmouth conference were an era of discovery, of sprinting across new ground. The programs that were developed during this time were, to most people, simply “astonishing”:[47] computers were solving algebra word problems, proving theorems in geometry and learning to speak English. Few at the time would have believed that such “intelligent” behavior by machines was possible at all.[48] Researchers expressed an intense optimism in private and in print, predicting that a fully intelligent machine would be built in less than 20 years.[49] Government agencies like ARPA poured money into the new field.[50]

There were many successful programs and new directions in the late 50s and 1960s. Among the most influential were these:

Many early AI programs used the same basic algorithm. To achieve some goal (like winning a game or proving a theorem), they proceeded step by step towards it (by making a move or a deduction) as if searching through a maze, backtracking whenever they reached a dead end. This paradigm was called “reasoning as search”.[51]

The principal difficulty was that, for many problems, the number of possible paths through the “maze” was simply astronomical (a situation known as a “combinatorial explosion”). Researchers would reduce the search space by using heuristics or “rules of thumb” that would eliminate those paths that were unlikely to lead to a solution.[52]

Newell and Simon tried to capture a general version of this algorithm in a program called the “General Problem Solver”.[53] Other “searching” programs were able to accomplish impressive tasks like solving problems in geometry and algebra, such as Herbert Gelernter’s Geometry Theorem Prover (1958) and SAINT, written by Minsky’s student James Slagle (1961).[54] Other programs searched through goals and subgoals to plan actions, like the STRIPS system developed at Stanford to control the behavior of their robot Shakey.[55]

An important goal of AI research is to allow computers to communicate in natural languages like English. An early success was Daniel Bobrow’s program STUDENT, which could solve high school algebra word problems.[56]

A semantic net represents concepts (e.g. “house”,”door”) as nodes and relations among concepts (e.g. “has-a”) as links between the nodes. The first AI program to use a semantic net was written by Ross Quillian[57] and the most successful (and controversial) version was Roger Schank’s Conceptual dependency theory.[58]

Joseph Weizenbaum’s ELIZA could carry out conversations that were so realistic that users occasionally were fooled into thinking they were communicating with a human being and not a program. But in fact, ELIZA had no idea what she was talking about. She simply gave a canned response or repeated back what was said to her, rephrasing her response with a few grammar rules. ELIZA was the first chatterbot.[59]

In the late 60s, Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that AI research should focus on artificially simple situations known as micro-worlds. They pointed out that in successful sciences like physics, basic principles were often best understood using simplified models like frictionless planes or perfectly rigid bodies. Much of the research focused on a “blocks world,” which consists of colored blocks of various shapes and sizes arrayed on a flat surface.[60]

This paradigm led to innovative work in machine vision by Gerald Sussman (who led the team), Adolfo Guzman, David Waltz (who invented “constraint propagation”), and especially Patrick Winston. At the same time, Minsky and Papert built a robot arm that could stack blocks, bringing the blocks world to life. The crowning achievement of the micro-world program was Terry Winograd’s SHRDLU. It could communicate in ordinary English sentences, plan operations and execute them.[61]

The first generation of AI researchers made these predictions about their work:

In June 1963, MIT received a $2.2 million grant from the newly created Advanced Research Projects Agency (later known as DARPA). The money was used to fund project MAC which subsumed the “AI Group” founded by Minsky and McCarthy five years earlier. DARPA continued to provide three million dollars a year until the 70s.[66]DARPA made similar grants to Newell and Simon’s program at CMU and to the Stanford AI Project (founded by John McCarthy in 1963).[67] Another important AI laboratory was established at Edinburgh University by Donald Michie in 1965.[68] These four institutions would continue to be the main centers of AI research (and funding) in academia for many years.[69]

The money was proffered with few strings attached: J. C. R. Licklider, then the director of ARPA, believed that his organization should “fund people, not projects!” and allowed researchers to pursue whatever directions might interest them.[70] This created a freewheeling atmosphere at MIT that gave birth to the hacker culture,[71] but this “hands off” approach would not last.

In the 70s, AI was subject to critiques and financial setbacks. AI researchers had failed to appreciate the difficulty of the problems they faced. Their tremendous optimism had raised expectations impossibly high, and when the promised results failed to materialize, funding for AI disappeared.[72] At the same time, the field of connectionism (or neural nets) was shut down almost completely for 10 years by Marvin Minsky’s devastating criticism of perceptrons.[73] Despite the difficulties with public perception of AI in the late 70s, new ideas were explored in logic programming, commonsense reasoning and many other areas.[74]

In the early seventies, the capabilities of AI programs were limited. Even the most impressive could only handle trivial versions of the problems they were supposed to solve; all the programs were, in some sense, “toys”.[75] AI researchers had begun to run into several fundamental limits that could not be overcome in the 1970s. Although some of these limits would be conquered in later decades, others still stymie the field to this day.[76]

The agencies which funded AI research (such as the British government, DARPA and NRC) became frustrated with the lack of progress and eventually cut off almost all funding for undirected research into AI. The pattern began as early as 1966 when the ALPAC report appeared criticizing machine translation efforts. After spending 20 million dollars, the NRC ended all support.[84] In 1973, the Lighthill report on the state of AI research in England criticized the utter failure of AI to achieve its “grandiose objectives” and led to the dismantling of AI research in that country.[85] (The report specifically mentioned the combinatorial explosion problem as a reason for AI’s failings.)[86]DARPA was deeply disappointed with researchers working on the Speech Understanding Research program at CMU and canceled an annual grant of three million dollars.[87] By 1974, funding for AI projects was hard to find.

Hans Moravec blamed the crisis on the unrealistic predictions of his colleagues. “Many researchers were caught up in a web of increasing exaggeration.”[88] However, there was another issue: since the passage of the Mansfield Amendment in 1969, DARPA had been under increasing pressure to fund “mission-oriented direct research, rather than basic undirected research”. Funding for the creative, freewheeling exploration that had gone on in the 60s would not come from DARPA. Instead, the money was directed at specific projects with clear objectives, such as autonomous tanks and battle management systems.[89]

Several philosophers had strong objections to the claims being made by AI researchers. One of the earliest was John Lucas, who argued that Gdel’s incompleteness theorem showed that a formal system (such as a computer program) could never see the truth of certain statements, while a human being could.[90]Hubert Dreyfus ridiculed the broken promises of the 60s and critiqued the assumptions of AI, arguing that human reasoning actually involved very little “symbol processing” and a great deal of embodied, instinctive, unconscious “know how”.[91][92]John Searle’s Chinese Room argument, presented in 1980, attempted to show that a program could not be said to “understand” the symbols that it uses (a quality called “intentionality”). If the symbols have no meaning for the machine, Searle argued, then the machine can not be described as “thinking”.[93]

These critiques were not taken seriously by AI researchers, often because they seemed so far off the point. Problems like intractability and commonsense knowledge seemed much more immediate and serious. It was unclear what difference “know how” or “intentionality” made to an actual computer program. Minsky said of Dreyfus and Searle “they misunderstand, and should be ignored.”[94] Dreyfus, who taught at MIT, was given a cold shoulder: he later said that AI researchers “dared not be seen having lunch with me.”[95]Joseph Weizenbaum, the author of ELIZA, felt his colleagues’ treatment of Dreyfus was unprofessional and childish. Although he was an outspoken critic of Dreyfus’ positions, he “deliberately made it plain that theirs was not the way to treat a human being.”[96]

Weizenbaum began to have serious ethical doubts about AI when Kenneth Colby wrote DOCTOR, a chatterbot therapist. Weizenbaum was disturbed that Colby saw his mindless program as a serious therapeutic tool. A feud began, and the situation was not helped when Colby did not credit Weizenbaum for his contribution to the program. In 1976, Weizenbaum published Computer Power and Human Reason which argued that the misuse of artificial intelligence has the potential to devalue human life.[97]

A perceptron was a form of neural network introduced in 1958 by Frank Rosenblatt, who had been a schoolmate of Marvin Minsky at the Bronx High School of Science. Like most AI researchers, he was optimistic about their power, predicting that “perceptron may eventually be able to learn, make decisions, and translate languages.” An active research program into the paradigm was carried out throughout the 60s but came to a sudden halt with the publication of Minsky and Papert’s 1969 book Perceptrons. It suggested that there were severe limitations to what perceptrons could do and that Frank Rosenblatt’s predictions had been grossly exaggerated. The effect of the book was devastating: virtually no research at all was done in connectionism for 10 years. Eventually, a new generation of researchers would revive the field and thereafter it would become a vital and useful part of artificial intelligence. Rosenblatt would not live to see this, as he died in a boating accident shortly after the book was published.[73]

Logic was introduced into AI research as early as 1958, by John McCarthy in his Advice Taker proposal.[98] In 1963, J. Alan Robinson had discovered a simple method to implement deduction on computers, the resolution and unification algorithm. However, straightforward implementations, like those attempted by McCarthy and his students in the late 60s, were especially intractable: the programs required astronomical numbers of steps to prove simple theorems.[99] A more fruitful approach to logic was developed in the 1970s by Robert Kowalski at the University of Edinburgh, and soon this led to the collaboration with French researchers Alain Colmerauer and Philippe Roussel who created the successful logic programming language Prolog.[100] Prolog uses a subset of logic (Horn clauses, closely related to “rules” and “production rules”) that permit tractable computation. Rules would continue to be influential, providing a foundation for Edward Feigenbaum’s expert systems and the continuing work by Allen Newell and Herbert A. Simon that would lead to Soar and their unified theories of cognition.[101]

Critics of the logical approach noted, as Dreyfus had, that human beings rarely used logic when they solved problems. Experiments by psychologists like Peter Wason, Eleanor Rosch, Amos Tversky, Daniel Kahneman and others provided proof.[102] McCarthy responded that what people do is irrelevant. He argued that what is really needed are machines that can solve problemsnot machines that think as people do.[103]

Among the critics of McCarthy’s approach were his colleagues across the country at MIT. Marvin Minsky, Seymour Papert and Roger Schank were trying to solve problems like “story understanding” and “object recognition” that required a machine to think like a person. In order to use ordinary concepts like “chair” or “restaurant” they had to make all the same illogical assumptions that people normally made. Unfortunately, imprecise concepts like these are hard to represent in logic. Gerald Sussman observed that “using precise language to describe essentially imprecise concepts doesn’t make them any more precise.”[104]Schank described their “anti-logic” approaches as “scruffy”, as opposed to the “neat” paradigms used by McCarthy, Kowalski, Feigenbaum, Newell and Simon.[105]

In 1975, in a seminal paper, Minsky noted that many of his fellow “scruffy” researchers were using the same kind of tool: a framework that captures all our common sense assumptions about something. For example, if we use the concept of a bird, there is a constellation of facts that immediately come to mind: we might assume that it flies, eats worms and so on. We know these facts are not always true and that deductions using these facts will not be “logical”, but these structured sets of assumptions are part of the context of everything we say and think. He called these structures “frames”. Schank used a version of frames he called “scripts” to successfully answer questions about short stories in English.[106] Many years later object-oriented programming would adopt the essential idea of “inheritance” from AI research on frames.

In the 1980s a form of AI program called “expert systems” was adopted by corporations around the world and knowledge became the focus of mainstream AI research. In those same years, the Japanese government aggressively funded AI with its fifth generation computer project. Another encouraging event in the early 1980s was the revival of connectionism in the work of John Hopfield and David Rumelhart. Once again, AI had achieved success.

An expert system is a program that answers questions or solves problems about a specific domain of knowledge, using logical rules that are derived from the knowledge of experts. The earliest examples were developed by Edward Feigenbaum and his students. Dendral, begun in 1965, identified compounds from spectrometer readings. MYCIN, developed in 1972, diagnosed infectious blood diseases. They demonstrated the feasibility of the approach.[107]

Expert systems restricted themselves to a small domain of specific knowledge (thus avoiding the commonsense knowledge problem) and their simple design made it relatively easy for programs to be built and then modified once they were in place. All in all, the programs proved to be useful: something that AI had not been able to achieve up to this point.[108]

In 1980, an expert system called XCON was completed at CMU for the Digital Equipment Corporation. It was an enormous success: it was saving the company 40 million dollars annually by 1986.[109] Corporations around the world began to develop and deploy expert systems and by 1985 they were spending over a billion dollars on AI, most of it to in-house AI departments. An industry grew up to support them, including hardware companies like Symbolics and Lisp Machines and software companies such as IntelliCorp and Aion.[110]

The power of expert systems came from the expert knowledge they contained. They were part of a new direction in AI research that had been gaining ground throughout the 70s. “AI researchers were beginning to suspectreluctantly, for it violated the scientific canon of parsimonythat intelligence might very well be based on the ability to use large amounts of diverse knowledge in different ways,”[111] writes Pamela McCorduck. “[T]he great lesson from the 1970s was that intelligent behavior depended very much on dealing with knowledge, sometimes quite detailed knowledge, of a domain where a given task lay”.[112]Knowledge based systems and knowledge engineering became a major focus of AI research in the 1980s.[113]

The 1980s also saw the birth of Cyc, the first attempt to attack the commonsense knowledge problem directly, by creating a massive database that would contain all the mundane facts that the average person knows. Douglas Lenat, who started and led the project, argued that there is no shortcut the only way for machines to know the meaning of human concepts is to teach them, one concept at a time, by hand. The project was not expected to be completed for many decades.[114]

Chess playing programs HiTech and Deep Thought defeated chess masters in 1989. Both were developed by Carnegie Mellon University; Deep Thought development paved the way for the Deep Blue.[115]

In 1981, the Japanese Ministry of International Trade and Industry set aside $850 million for the Fifth generation computer project. Their objectives were to write programs and build machines that could carry on conversations, translate languages, interpret pictures, and reason like human beings.[116] Much to the chagrin of scruffies, they chose Prolog as the primary computer language for the project.[117]

Other countries responded with new programs of their own. The UK began the 350 million Alvey project. A consortium of American companies formed the Microelectronics and Computer Technology Corporation (or “MCC”) to fund large scale projects in AI and information technology.[118][119]DARPA responded as well, founding the Strategic Computing Initiative and tripling its investment in AI between 1984 and 1988.[120]

In 1982, physicist John Hopfield was able to prove that a form of neural network (now called a “Hopfield net”) could learn and process information in a completely new way. Around the same time, David Rumelhart popularized a new method for training neural networks called “backpropagation” (discovered years earlier by Paul Werbos). These two discoveries revived the field of connectionism which had been largely abandoned since 1970.[119][121]

The new field was unified and inspired by the appearance of Parallel Distributed Processing in 1986a two volume collection of papers edited by Rumelhart and psychologist James McClelland. Neural networks would become commercially successful in the 1990s, when they began to be used as the engines driving programs like optical character recognition and speech recognition.[119][122]

The business community’s fascination with AI rose and fell in the 80s in the classic pattern of an economic bubble. The collapse was in the perception of AI by government agencies and investors the field continued to make advances despite the criticism. Rodney Brooks and Hans Moravec, researchers from the related field of robotics, argued for an entirely new approach to artificial intelligence.

The term “AI winter” was coined by researchers who had survived the funding cuts of 1974 when they became concerned that enthusiasm for expert systems had spiraled out of control and that disappointment would certainly follow.[123] Their fears were well founded: in the late 80s and early 90s, AI suffered a series of financial setbacks.

The first indication of a change in weather was the sudden collapse of the market for specialized AI hardware in 1987. Desktop computers from Apple and IBM had been steadily gaining speed and power and in 1987 they became more powerful than the more expensive Lisp machines made by Symbolics and others. There was no longer a good reason to buy them. An entire industry worth half a billion dollars was demolished overnight.[124]

Eventually the earliest successful expert systems, such as XCON, proved too expensive to maintain. They were difficult to update, they could not learn, they were “brittle” (i.e., they could make grotesque mistakes when given unusual inputs), and they fell prey to problems (such as the qualification problem) that had been identified years earlier. Expert systems proved useful, but only in a few special contexts.[125]

In the late 80s, the Strategic Computing Initiative cut funding to AI “deeply and brutally.” New leadership at DARPA had decided that AI was not “the next wave” and directed funds towards projects that seemed more likely to produce immediate results.[126]

By 1991, the impressive list of goals penned in 1981 for Japan’s Fifth Generation Project had not been met. Indeed, some of them, like “carry on a casual conversation” had not been met by 2010.[127] As with other AI projects, expectations had run much higher than what was actually possible.[127]

In the late 80s, several researchers advocated a completely new approach to artificial intelligence, based on robotics.[128] They believed that, to show real intelligence, a machine needs to have a body it needs to perceive, move, survive and deal with the world. They argued that these sensorimotor skills are essential to higher level skills like commonsense reasoning and that abstract reasoning was actually the least interesting or important human skill (see Moravec’s paradox). They advocated building intelligence “from the bottom up.”[129]

The approach revived ideas from cybernetics and control theory that had been unpopular since the sixties. Another precursor was David Marr, who had come to MIT in the late 70s from a successful background in theoretical neuroscience to lead the group studying vision. He rejected all symbolic approaches (both McCarthy’s logic and Minsky’s frames), arguing that AI needed to understand the physical machinery of vision from the bottom up before any symbolic processing took place. (Marr’s work would be cut short by leukemia in 1980.)[130]

In a 1990 paper, “Elephants Don’t Play Chess,”[131] robotics researcher Rodney Brooks took direct aim at the physical symbol system hypothesis, arguing that symbols are not always necessary since “the world is its own best model. It is always exactly up to date. It always has every detail there is to be known. The trick is to sense it appropriately and often enough.”[132] In the 80s and 90s, many cognitive scientists also rejected the symbol processing model of the mind and argued that the body was essential for reasoning, a theory called the embodied mind thesis.[133]

The field of AI, now more than a half a century old, finally achieved some of its oldest goals. It began to be used successfully throughout the technology industry, although somewhat behind the scenes. Some of the success was due to increasing computer power and some was achieved by focusing on specific isolated problems and pursuing them with the highest standards of scientific accountability. Still, the reputation of AI, in the business world at least, was less than pristine. Inside the field there was little agreement on the reasons for AI’s failure to fulfill the dream of human level intelligence that had captured the imagination of the world in the 1960s. Together, all these factors helped to fragment AI into competing subfields focused on particular problems or approaches, sometimes even under new names that disguised the tarnished pedigree of “artificial intelligence”.[134] AI was both more cautious and more successful than it had ever been.

On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov.[135] The super computer was a specialized version of a framework produced by IBM, and was capable of processing twice as many moves per second as it had during the first match (which Deep Blue had lost), reportedly 200,000,000 moves per second. The event was broadcast live over the internet and received over 74 million hits.[136]

In 2005, a Stanford robot won the DARPA Grand Challenge by driving autonomously for 131 miles along an unrehearsed desert trail.[137] Two years later, a team from CMU won the DARPA Urban Challenge by autonomously navigating 55 miles in an Urban environment while adhering to traffic hazards and all traffic laws.[138] In February 2011, in a Jeopardy! quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.[139]

These successes were not due to some revolutionary new paradigm, but mostly on the tedious application of engineering skill and on the tremendous power of computers today.[140] In fact, Deep Blue’s computer was 10 million times faster than the Ferranti Mark 1 that Christopher Strachey taught to play chess in 1951.[141] This dramatic increase is measured by Moore’s law, which predicts that the speed and memory capacity of computers doubles every two years. The fundamental problem of “raw computer power” was slowly being overcome.

A new paradigm called “intelligent agents” became widely accepted during the 90s.[142] Although earlier researchers had proposed modular “divide and conquer” approaches to AI,[143] the intelligent agent did not reach its modern form until Judea Pearl, Allen Newell and others brought concepts from decision theory and economics into the study of AI.[144] When the economist’s definition of a rational agent was married to computer science’s definition of an object or module, the intelligent agent paradigm was complete.

An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. By this definition, simple programs that solve specific problems are “intelligent agents”, as are human beings and organizations of human beings, such as firms. The intelligent agent paradigm defines AI research as “the study of intelligent agents”. This is a generalization of some earlier definitions of AI: it goes beyond studying human intelligence; it studies all kinds of intelligence.[145]

The paradigm gave researchers license to study isolated problems and find solutions that were both verifiable and useful. It provided a common language to describe problems and share their solutions with each other, and with other fields that also used concepts of abstract agents, like economics and control theory. It was hoped that a complete agent architecture (like Newell’s SOAR) would one day allow researchers to build more versatile and intelligent systems out of interacting intelligent agents.[144][146]

AI researchers began to develop and use sophisticated mathematical tools more than they ever had in the past.[147] There was a widespread realization that many of the problems that AI needed to solve were already being worked on by researchers in fields like mathematics, economics or operations research. The shared mathematical language allowed both a higher level of collaboration with more established and successful fields and the achievement of results which were measurable and provable; AI had become a more rigorous “scientific” discipline. Russell & Norvig (2003) describe this as nothing less than a “revolution” and “the victory of the neats”.[148][149]

Judea Pearl’s highly influential 1988 book[150] brought probability and decision theory into AI. Among the many new tools in use were Bayesian networks, hidden Markov models, information theory, stochastic modeling and classical optimization. Precise mathematical descriptions were also developed for “computational intelligence” paradigms like neural networks and evolutionary algorithms.[148]

Algorithms originally developed by AI researchers began to appear as parts of larger systems. AI had solved a lot of very difficult problems[151] and their solutions proved to be useful throughout the technology industry,[152] such as data mining, industrial robotics, logistics,[153]speech recognition,[154] banking software,[155] medical diagnosis[155] and Google’s search engine.[156]

The field of AI receives little or no credit for these successes. Many of AI’s greatest innovations have been reduced to the status of just another item in the tool chest of computer science.[157]Nick Bostrom explains “A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it’s not labeled AI anymore.”[158]

Many researchers in AI in 1990s deliberately called their work by other names, such as informatics, knowledge-based systems, cognitive systems or computational intelligence. In part, this may be because they considered their field to be fundamentally different from AI, but also the new names help to procure funding. In the commercial world at least, the failed promises of the AI Winter continue to haunt AI research, as the New York Times reported in 2005: “Computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild-eyed dreamers.”[159][160][161]

In 1968, Arthur C. Clarke and Stanley Kubrick had imagined that by the year 2001, a machine would exist with an intelligence that matched or exceeded the capability of human beings. The character they created, HAL 9000, was based on a belief shared by many leading AI researchers that such a machine would exist by the year 2001.[162]

Marvin Minsky asks “So the question is why didn’t we get HAL in 2001?”[163] Minsky believes that the answer is that the central problems, like commonsense reasoning, were being neglected, while most researchers pursued things like commercial applications of neural nets or genetic algorithms. John McCarthy, on the other hand, still blames the qualification problem.[164] For Ray Kurzweil, the issue is computer power and, using Moore’s Law, he predicts that machines with human-level intelligence will appear by 2029.[165]Jeff Hawkins argues that neural net research ignores the essential properties of the human cortex, preferring simple models that have been successful at solving simple problems.[166] There are many other explanations and for each there is a corresponding research program underway.

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Intro to Artificial Intelligence Course and Training Online …

Posted: July 5, 2016 at 11:42 pm

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Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, youll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course youll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.

See the Technology Requirements for using Udacity.

Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

This class is self paced. You can begin whenever you like and then follow your own pace. Its a good idea to set goals for yourself to make sure you stick with the course.

This class will always be available!

Take a look at the Class Summary, What Should I Know, and What Will I Learn sections above. If you want to know more, just enroll in the course and start exploring.

Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that youre confused, you can always go back and watch something that you skipped.

Its completely free! If youre feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.

Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.

Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc… But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.

Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.

Nanodegree is a trademark of Udacity 20112016 Udacity, Inc.

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Intro to Artificial Intelligence Course and Training Online …

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Intro to Artificial Intelligence Course and Training …

Posted: June 28, 2016 at 2:46 am

Watch Video

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, youll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course youll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.

See the Technology Requirements for using Udacity.

Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

This class is self paced. You can begin whenever you like and then follow your own pace. Its a good idea to set goals for yourself to make sure you stick with the course.

This class will always be available!

Take a look at the Class Summary, What Should I Know, and What Will I Learn sections above. If you want to know more, just enroll in the course and start exploring.

Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that youre confused, you can always go back and watch something that you skipped.

Its completely free! If youre feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.

Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.

Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc… But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.

Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.

Nanodegree is a trademark of Udacity 20112016 Udacity, Inc.

Continue reading here:

Intro to Artificial Intelligence Course and Training …

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