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The Evolutionary Perspective
Category Archives: Artificial Intelligence
Posted: February 26, 2017 at 11:17 pm
For the past year, we as a society have been worried sick about artificial intelligence eating the jobs of 3 million truck drivers. Turns out that a more imminently endangered species are the Wall Street traders and hedge fund managers who can afford to buy Lamborghinis and hire Elton John to play their Hamptons house parties.
So maybe hooray for AI on this one?
Financial giants such as Goldman Sachs and many of the biggest hedge funds are all switching on AI-driven systems that can foresee market trends and make trades better than humans. Its been happening, drip by drip, for years, but a torrent of AI is about to wash through the industry, says Mark Minevich, a New York-based investor in AI and senior adviser to the U.S. Council on Competitiveness. High-earning traders are going to get unceremoniously dumped like workers at a closing factory.
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It will really hit at the soul of Wall Street, Minevich tells me. It will transform New York.
Some of these AI trading systems are being built by startups such as Sentient in San Francisco and Aidyia in Hong Kong. In 2014, Goldman Sachs invested in and began installing an AI-driven trading platform called Kensho. Walnut Algorithms, a startup hedge fund, was designed from the beginning to work on AI. Infamously weird hedge fund company Bridgewater Associates hired its own team to build an AI system that could practically run the operation on its own. Bridgewaters effort is headed by David Ferrucci, who previously led IBMs development of the Watson computer that won on Jeopardy!
AI trading software can suck up enormous amounts of data to learn about the world and then make predictions about stocks, bonds, commodities and other financial instruments. The machines can ingest books, tweets, news reports, financial data, earnings numbers, international monetary policy, even Saturday Night Live sketchesanything that might help the software understand global trends. The AI can keep watching this information all the time, never tiring, always learning and perfecting its predictions.
RELATED: How robots will save the global economy
A report from Eurekahedge monitored 23 hedge funds utilizing AI and found they outperformed funds relying on people. Quants, the Ph.D. mathematicians who design fancy statistical models, have been the darlings of hedge funds for the past decade, yet they rely on crunching historical data to create a model that can anticipate market trends. AI can do that too, but AI can then watch up-to-the-instant data and learn from it to continually improve its model. In that way, quant models are like a static medical textbook, while AI learning machines are like a practicing doctor who keeps up with the latest research. Which is going to lead to a better diagnosis? Trading models built using back-tests on historical data have often failed to deliver good returns in real time, says the Eurekahedge report.
Traders work on the floor of the New York Stock Exchange (NYSE) as the Dow Jones industrial average closed above the 20,000 mark for the first time on January 25 in New York City. Spencer Platt/Getty
Human traders and hedge fund managers dont stand a chance, in large part because theyre human. Humans have biases and sensitivities, conscious and unconscious,” says Babak Hodjat, co-founder of Sentient and a computer scientist who played a role in Apples development of Siri. “It’s well-documented we humans make mistakes. For me, it’s scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you.”
So whats going to happen to the finance people who find themselves standing in front of the oncoming AI bus? Well, average compensation for staff in sales, trading and research at the 12 largest investment banks is $500,000, according to business intelligence company Coalition Development. Many traders earn in the millions. In 2015, five hedge fund managers made $1 billion or more, according to an industry survey. If you think Carls Jr. is motivated to replace $8-an-hour fast-food workers with robots, imagine the motivation to dump million-dollar-a-year ($500 an hour!) traders.
Goldman Sachs shows just how devastating automation can be to traders. In 2000, its U.S. cash equities trading desk in New York employed 600 traders. Today, that operation has two equity traders, with machines doing the rest. And this is before the full brunt of AI has come into play at Goldman. In 10 years, Goldman Sachs will be significantly smaller by head count than it is today, Daniel Nadler, CEO of Kensho, told The New York Times. Expect the same to happen on every trading floor at every major financial company.
Much of America is not going to weep for the types of people depicted in The Wolf of Wall Street, yet this new AI reality could be devastating in many ways. Imagine the impact on high-end real estate in New York. Think of the For Sale signs on summer beach homes in Southampton. How will luxury retailers survive the likely dip in sales of $2,000 suits and $5,900-per-pound white truffles? Maybe Donald Trump will be driven to demand that somebody bring back traders jobs, thinking theyve moved to Mexico.
Minevich, though, sees a net positive if AI drives brilliant people out of finance and into, well, almost anything else.
As the surest, fastest path to million-dollar paydays, Wall Street trading and hedge fund managing have long soaked up a large chunk of Americas best and brightest. About one-third of graduates from the top 10 business schools go into finance. Only a tiny sliver, usually around 5 percent, go into health care. An even smaller percentage go into energy or manufacturing businesses, and you can count on two hands the number who take jobs at nonprofits each year.
Most of the rest of society looks at that and sees selfishness. Yeah, sure, we need liquid markets and financial instruments and all that. But if were going to pay a group of people so much money, maybe wed be better off if they were inventing electric cars that go 1,000 miles on a charge, or healthy vegetarian kielbasa, or babies who dont cry on airplanes. Just do something that brings tangible benefits to the masses.
Some of these smart people will move into tech startups, or will help develop more AI platforms, or autonomous cars, or energy technology, Minevich says. That could be really helpful right now, since the tech industry is always fretting that it doesnt have enough highly skilled pros and might be facing a geek drought in the age of Trump travel bans. If the MBA elite leave Wall Street but stay in New York, Minevich adds, New York might compete with Silicon Valley in tech.
As math Ph.D.s no longer find that hedge fund recruiters are salivating over them, they might leap into efforts to model climate change or the behavior of cancer cells in the body. The National Security Agencys website says it is actively seeking mathematicians to work on some of our hardest signals intelligence and information security problems. Math whizzes could help catch terrorists! Or liberals!
The pay for a mathematician at the National Security Agency is around $100,000. Compared with a hedge fund salary, that would be a major lifestyle downgrade. But at least the traders and quants will have options, which is more than we can say for truck drivers and other workers threatened by AI.
Theres one other benefit to AI machines taking over finance. Ben Goertzel, chief scientist at Aidyia, says his machine will never need human intervention. If we all die, it would keep trading, he once said.
So if Trump pulls out the nuclear codes and pushes the button, at least some people will still get a good return on their 401(k)s.
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Posted: at 11:17 pm
How Artificial Intelligence Can Benefit E-Commerce Businesses
Unless you've been on a sabbatical deep in the rainforests of Peru, you've probably heard about Artificial Intelligence (AI). But if you still relate it to all things science fiction and robotic, it's time to look further. Whether you know it or not …
Posted: at 11:17 pm
The government will launch a review into Artifical Intelligence (AI) and robotics in an attempt to make the UK a world leader in tech.
The government said in a statement on Sunday that it would invest 17.3 million in university research on AI.Artificial intelligence powers technologies such as Apples SIRI, Amazons Alexa, and driverless cars.
According to a report by consultancy firm Accenture, Artificial Intelligence could add around 654 billion to the UK economy.
A report by the Institute for Public Policy Research recently forecast that millions of jobs will be lost to automation over the next two decades. Researchers predicted that two million jobs retail jobs will disappear by 2030 and 600,000 will go in manufacturing.
Jrme Pesenti, CEO of Benevolent Tech, who will be leading government research into AI, said,
There has been a lot of unwarranted negative hype around Artificial Intelligence (AI), but it has the ability to drive enormous growth for the UK economy, create jobs, foster new skills, positively transform every industry and retain Britains status as a world leader in innovative technology.
EU universal income must be ‘seriously considered’ amid rise of robots
The announcement is part of the governments new Digital Strategy, which will be announced in full on Wednesday. As well as investment in research and the tech industry, the strategy is also expected to detail a comprehensive modernisation of the civil service.
The government has been heavily criticised the delay in the publication of the strategy. In 2015, Ed Vaizey, the then DigitalMinister, said plans would be published in early 2016.
In January, the chairman of the governments Science and Technology Committee criticised the government for this delay.
In a letter to Digital Minister Matt Hancock, Mr Metcalfe expressed his disappointment over such a long delay.
The letter also asked why the strategy continues to be a work in progress nearly a year after [Mr Hancocks] predecessor considered it already largely completed.
The government has said it was forced to delay the publication of the report to take into account the impact of Brexit.
However, other sources have suggested that Whitehalls resistance to the modernisation of the civil service under the Government Digital Service plans was also a significant factor.
Why artificial intelligence is about to get real backing in the government’s Digital Strategy – City A.M.
Posted: at 11:17 pm
Artificial intelligence (AI) has long captured peoples imaginations.
It has been a theme of science fiction novels and films for more than half a century and, while reality has always seemed a long way behind the fiction, that is now changing.
AI is finally living up to its promise and has the potential to improve almost every aspect of our lives. Indeed, entire industries of which we cannot yet conceive might be created.
Britain now has an opportunity to become the world-leader in this technology, to shape the revolution, create thousands of jobs, and transform lives for the better.
Read more: Watch out Alexa: Google’s AI voice assistant’s been released into the wild
Accenture has estimated AI could add in the region of 654bn to the UK economy by 2035. This week, we will publish the governments Digital Strategy, which will set out clearly how we intend to capitalise on that potential.
Properly deployed, AI has the potential to make us healthier and more efficient. It is starting to help diagnose diseases with greater accuracy and identify when elderly people are likely to fall, by studying their movements. It can determine when traffic lights or speed limits need to change to keep traffic flowing. It can predict when equipment in factories is likely to fail and should be replaced.
AI is changing our world, and will continue to do so in coming years. This undeniably presents a challenge, but it is also a huge opportunity. The challenge is to ensure the technology develops with proper safeguards and in a way the public support.
Read more: Ford has made a major AI investment to boost its self-driving offerings
The opportunity is that Britain is at the forefront of shaping the AI revolution and reaping the benefits, including new jobs and more growth.
I want the UK to lead the way. We are already pioneers in this exciting technology. We have some of the best minds in the world, working in some of the worlds best universities. We have earned a reputation for brilliance in AI. Consequently, some of the most exciting companies at the cutting edge of AI, such as Babylon Health, Onfido and Improbable are based here.
The governments Industrial Strategy set out how we will back Britain for the long term by building upon strategic strengths so businesses can grow and create more high-skilled, high-paid jobs. AI is one of those strengths, so we want to hear how government and industry can work together to support it.
Read more: Watch out Siri? Samsung’s next phone will have an AI assistant
As ever more decisions affecting our lives are taken by computers, we must get the rules right. People must have confidence in AI if we are to embrace it.
That confidence is dependent on proper frameworks and safeguards. People need to know the machines are not making up the rules as they go.
As the lead minister for the digital economy, I am determined to get this right. That will require some clear principles, whether they are about preventing systems driven by AI from importing prejudices, or ensuring that decision-making remains accountable.
Read more: The US is embracing AI’s opportunities while the rest of the world frets
And there will of course be many areas when we decide we always need humans in decision-making processes.
If we get this right, we can make sure we all benefit from AIs potential to improve our lives. We can find the sweet spot where the tech can develop in a way that people continue to support.
The Royal Society and British Academy have started looking at this issue. We look forward to seeing the results of their work, and will build on this with others as we think about the principles and frameworks that we will need to put in place.
Read more: Robots, AI and digital disruption are coming to the hedge fund industry
This country has a proud history of technological innovation and of making technology work for us, and we have a government that understands that our future prosperity depends on innovation. But that does not mean we can rest on our laurels. I am delighted that Professor Dame Wendy Hall and Jerome Pesenti have agreed to review what Britain needs to do to stay ahead on AI, encompassing everything from skills to investment. Between them, Wendy and Jerome have a wealth of expertise in academia and business, and are perfectly placed to lead this work.
And we have also made emerging technologies such as AI a key part of the UKs Digital Strategy. It outlines our vision of a digital economy that works for all our citizens and how to achieve it. And it includes 17.3m of new funding to keep British universities at the forefront of pioneering robotics and AI research.
The digital revolution is happening and it is speeding up. Instead of getting left behind, we can make it work for everyone in the UK and lead the world.
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Posted: at 11:17 pm
For more than 30 years, Gibbs has advised on and developed product and service marketing for many businesses and he has consulted, lectured, and authored numerous articles and books.
I just binge-listened to an outstanding podcast, LifeAfter, which, without giving too much away, is about artificial intelligence and its impact on people. Here’s the show’s synopsis:
When you die in the digital age, pieces of you live on forever. In your emails, your social media posts and uploads, in the texts and videos youve messaged, and for some even in their secret online lives few even know about. But what if that digital existence took on a life of its own? Ross, a low level FBI employee, faces that very question as he starts spending his days online talking to his wife Charlie, who died8 months ago
The ethical issues that this podcast raises are fascinating and riff on some of the AI-related issues we’re starting to appreciate.
One of the big issues in the real world we’re just getting to grips with lies in the way we humans create intelligent systems because whoever does the design and coding brings their own world views, biases, misunderstandings, and, most crucially, prejudices to the party.
A great example of this kind of problem in current AI products was discussed in a recent Quartz article, We tested bots like Siri and Alexa to see who would stand up to sexual harassment. The results of this testing are fascinating and, to some extent, predictable:
ApplesSiri, AmazonsAlexa, MicrosoftsCortana, and GooglesGoogle Homepeddle stereotypes of female subserviencewhich puts theirprogressiveparent companies in a moral predicament The message is clear: Instead of fighting back against abuse, each bot helps entrench sexist tropes through their passivity.
Now some AI apologists might argue that we’re in the earliest days of this technology and the scope of what is required to deliver a general-purpose interactive digital assistant is still being explored so weaknesses and oversights are to be expected and will be fixed, all in good time. Indeed, given the sheer magnitude of the work, this argument doesn’t, on the face of it, seem unreasonable but the long-term problem is to what extent these deficiencies will become “baked-in” to these products such that they can never be wholly fixed and subtle bias on a topic or position is often more effective in reinforcing belief and behavior than explicit support. Moreover, given that humans prefer to have their prejudices affirmed and supported and that to be really effective their digital assistants will have to learn what their masters want and expect, there’s a risk of self-reinforcing feedback.
The danger of baked-in acceptance and even support of sexist tropes is obviously bad in intelligent assistants but when AI is applied to life-changing real-world problems, the subtlest built-in bias will become dangerous. How dangerous? Consider the non-AI, statistics-based algorithms that have for some years been used to derive “risk assessments” of criminals as discussed in Pro Publica’s article Machine Bias, published last year. These algorithmic assessments what are, essentially, “predictive policing” (need I mention “pre-crime”?) determine everything from whether someone can get bail and for how much, to how harsh their sentence will be.
[Pro Publica] obtained the risk scores assigned to more than 7,000 people arrested in Broward County, Florida, in 2013 and 2014 and checked to see how many were charged with new crimes over the next two years, thesame benchmark usedby the creators of the algorithm.
The score proved remarkably unreliable in forecasting violent crime: Only 20 percent of the people predicted to commit violent crimes actually went on to do so.
When a full range of crimes were taken into account including misdemeanors such as driving with an expired license the algorithm was somewhat more accurate than a coin flip. Of those deemed likely to re-offend, 61 percent were arrested for any subsequent crimes within two years.
That’s bad enough but a sadly predictable built-in bias was revealed:
In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways.
The impetus to use algorithms to handle complex, expensive problems in services such as the cash-strapped court system is obvious and even when serious flaws are identified in these systems, there’s huge opposition to stopping their use because these algorithms give the illusion of solving a high-level system problems (consistency of judgments, cost, and speed of process) even though the consequences to individuals (disproportionate loss of freedom) are clear to everyone and life-changing for those affected.
Despite these well-known problems with risk assessment algorithms there’s absolutely no doubt that AI-based solutions that rely on Big Data and deep learning are destined to become de rigueur and the biases and prejudices baked-in to those systems will be much harder to spot.
Will these AI systems be more objective than humans in quantifying risk and determining outcomes? Is it fair to use what will be alien intelligences to determine the course of people’s lives?
My fear is that the sheer impenetrability of AI systems, the lack of understanding by those who will use them, and the “Wow factor” of AI will make their adoption not an “if” but a “when” that will be much closer than we might imagine and the result will be a great ethical void that will support even greater discrimination, unfair treatment, and expediency in an already deeply flawed justice system.
We know that this is a highly likely future. What are we going to do about it?
Comments? Thoughts? Drop me a line then follow me on Twitter and Facebook and sign up for my newsletter!
Posted: at 11:17 pm
Artificial intelligence is perhaps one of the most intriguing forms of technology our society has seen in quite some time. Very few people are aware of what AI is capable of already, though. Over the past few years, artificial intelligence platforms have been responsible for creating various pop music songs. Although none of these tracks ever hit the billboards, it goes to show even musicians may be replaced by robots in the future.
Although the name of the song would not hint at artificial intelligence being involved, the entire song is composed by an AI solution. Listening closely to this track reveals some intriguing similarities to the music created by The Beatles many moons ago. Imitation is a fine form of flattery, yet it is also creepy to think of robots and software being capable of creating better music than human artists.
With the help of Flow Machines, composers created the Mr. Shadow track. Although this track is not entirely composed by AI, it played a big role in the process. Arranging and production is still done by a human, as are the written lyrics. However, the involvement of AI cannot be denied where this particular track is concerned. It is intriguing to see how composers try to embrace artificial intelligence, rather than oppose it.
Albeit this is not the name of an actual song, Project Magenta is one of Googles many ventures into the world of artificial intelligence. The platform will use state of the art machine intelligence to generate music and art. It is unclear how far artists and musicians can go with this technology, as there are seemingly no limitations as to what can be achieved.
A song was demonstrated using this technology back in 2016, which required the AI solution to be fed several recordings of different songs. As the machine learning tool was exposed to multiple examples its neural network started to piece together melodies on its own. As more time progresses, generating entire songs of its own accord will be second nature to Project Magenta.
It has to be said, the Nasciturus composition is quite a unique rendition in the world of computer-driven music. It is created by Iamus, a computer cluster located in the University of Malaga. A total of ten compositions were created by Iamus as part of its debut album, and required no human input whatever. To be more precise, it only required the initial programming before the AI went to work.
Nasciturus is labeled as evolutionary music which requires the use of a complex algorithm to turn a small initial input into a full-fledged composition. As time progresses, Iamus was capable of increasing the complexity of the input. Interestingly enough, this entire decision-making process takes less than a second. Rendering the music into formats humans can comprehend takes eight minutes, though.
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Posted: February 25, 2017 at 3:21 pm
Illustration by Michael George Haddad for Fortune
Outlier.ai , an artificial intelligence startup created by Flurry co-founder Sean Byrnes, has raised $2.2 million from Susa Ventures, Homebrew and First Round Capital.
Alongside co-founder Mike Kim, Byrnes started the company because, in the ten years he worked on Flurry (before selling it to Yahoo for a reported $240 million in 2014), he heard a common complaint about big data from customers: What does it all mean?
Today every part of your business is a fountain of data and it has gotten so bad that the companies dont know what to look for, Byrnes says. This idea might sound familiar to Fortune readers. Last week, the founders of Fika Ventures gave me a nearly identical quote . Thats no accident. Eva Ho of Fika is an investor in Outlier via her prior firm, Susa Ventures.
Outliers software, which integrates across all of a companys various tools (ZenDesk, Adwords, Adobe Analytics, etc), spits out stories about the data that allows workers not just statistics experts to use it to make decisions. The companys tools compete with offerings from IBM , Google Analytics and Mixpanel, but has an advantage because those tools do not work across many different systems. In five years, we will look back at companies that had five dozen dashboards, and it will look as outdated as using a paper map, Byrnes says.
Based in Oakland, Outlier has been offering its product to six customers in private beta since last year. It opens up to the general public today.
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Posted: at 3:21 pm
Artificial intelligence: here’s what you need to know to understand how machines learn
From Jeopardy winners and Go masters to infamous advertising-related racial profiling, it would seem we have entered an era in which artificial intelligence developments are rapidly accelerating. But a fully sentient being whose electronic brain can fully engage in complex cognitive tasks using fair moral judgement remains, for now, beyond our capabilities.
Unfortunately, current developments are generating a general fear of what artificial intelligence could become in the future. Its representation in recent pop culture shows how cautious and pessimistic we are about the technology. The problem with fear is that it can be crippling and, at times, promote ignorance.
Learning the inner workings of artificial intelligence is an antidote to these worries. And this knowledge can facilitate both responsible and carefree engagement.
The core foundation of artificial intelligence is rooted in machine learning, which is an elegant and widely accessible tool. But to understand what machine learning means, we first need to examine how the pros of its potential absolutely outweigh its cons.
Data are the key
Simply put, machine learning refers to teaching computers how to analyse data for solving particular tasks through algorithms. For handwriting recognition, for example, classification algorithms are used to differentiate letters based on someones handwriting. Housing data sets, on the other hand, use regression algorithms to estimate in a quantifiable way the selling price of a given property.
What would a machine say to this? Jonathan Khoo/Flickr, CC BY-NC-ND
Machine learning, then, comes down to data. Almost every enterprise generates data in one way or another: think market research, social media, school surveys, automated systems. Machine learning applications try to find hidden patterns and correlations in the chaos of large data sets to develop models that can predict behaviour.
Data have two key elements samples and features. The former represents individual elements in a group; the latter amounts to characteristics shared by them.
Look at social media as an example: users are samples and their usage can be translated as features. Facebook, for instance, employs different aspects of liking activity, which change from user to user, as important features for user-targeted advertising.
Facebook friends can also be used as samples, while their connections to other people act as features, establishing a network where information propagation can be studied.
My Facebook friends network: each node is a friend who might or might not be connected to other friends. The larger the node, the more connections one has. Similar colours indicate similar social circles. https://lostcircles.com/
Outside of social media, automated systems used in industrial processes as monitoring tools use time snapshots of the entire process as samples, and sensor measurements at a particular time as features. This allows the system to detect anomalies in the process in real time.
All these different solutions rely on feeding data to machines and teaching them to reach their own predictions once they have strategically assessed the given information. And this is machine learning.
Human intelligence as a starting point
Any data can be translated into these simple concepts and any machine-learning application, including artificial intelligence, uses these concepts as its building blocks.
Once data are understood, its time to decide what do to with this information. One of the most common and intuitive applications of machine learning is classification. The system learns how to put data into different groups based on a reference data set.
This is directly associated with the kinds of decisions we make every day, whether its grouping similar products (kitchen goods against beauty products, for instance), or choosing good films to watch based on previous experiences. While these two examples might seem completely disconnected, they rely on an essential assumption of classification: predictions defined as well-established categories.
When picking up a bottle of moisturiser, for example, we use a particular list of features (the shape of the container, for instance, or the smell of the product) to predict accurately that its a beauty product. A similar strategy is used for picking films by assessing a list of features (the director, for instance, or the actor) to predict whether a film is in one of two categories: good or bad.
By grasping the different relationships between features associated with a group of samples, we can predict whether a film may be worth watching or, better yet, we can create a program to do this for us.
But to be able to manipulate this information, we need to be a data science expert, a master of maths and statistics, with enough programming skills to make Alan Turing and Margaret Hamilton proud, right? Not quite.
You dont have to be Alan Turing to have a go at machine learning. CyberHades/Flickr, CC BY-NC
We all know enough of our native language to get by in our daily lives, even if only a few of us can venture into linguistics and literature. Maths is similar; its around us all the time, so calculating change from buying something or measuring ingredients to follow a recipe is not a burden. In the same way, machine-learning mastery is not a requirement for its conscious and effective use.
Yes, there are extremely well-qualified and expert data scientists out there but, with little effort, anyone can learn its basics and improve the way they see and take advantage of information.
Algorithm your way through it
Going back to our classification algorithm, lets think of one that mimics the way we make decisions. We are social beings, so how about social interactions? First impressions are important and we all have an internal model that evaluates in the first few minutes of meeting someone whether we like them or not.
Two outcomes are possible: a good or a bad impression. For every person, different characteristics (features) are taken into account (even if unconsciously) based on several encounters in the past (samples). These could be anything from tone of voice to extroversion and overall attitude to politeness.
For every new person we encounter, a model in our heads registers these inputs and establishes a prediction. We can break this modelling down to a set of inputs, weighted by their relevance to the final outcome.
For some people, attractiveness might be very important, whereas for others a good sense of humour or being a dog person says way more. Each person will develop her own model, which depends entirely on her experiences, or her data.
Different data result in different models being trained, with different outcomes. Our brain develops mechanisms that, while not entirely clear to us, establish how these factors will weight out.
What machine learning does is develop rigorous, mathematical ways for machines to calculate those outcomes, particularly in cases where we cannot easily handle the volume of data. Now more than ever, data are vast and everlasting. Having access to a tool that actively uses this data for practical problem solving, such as artificial intelligence, means everyone should and can explore and exploit this. We should do this not only so we can create useful applications, but also to put machine learning and artificial intelligence in a brighter and not so worrisome perspective.
There are several resources out there for machine learning although they do require some programming ability. Many popular languages tailored for machine learning are available, from basic tutorials to full courses. It takes nothing more than an afternoon to be able to start venturing into it with palpable results.
All this is not to say that the concept of machines with human-like minds should not concern us. But knowing more about how these minds might work will gives us the power to be agents of positive change in a way that can allow us to maintain control over artificial intelligence and not the other way around.
Matt Escobar receives funding from the Core Research for Evolutionary Science and Technology (CREST) project ‘Development of a knowledge-generating platform driven by big data in drug discovery through production processes’ of the Japan Science and Technology Agency (JST)
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Posted: February 24, 2017 at 6:26 pm
What Companies Are Winning The Race For Artificial Intelligence?
… general AI research, including traditional software engineers to build infrastructure and tooling, UX designers to help make research tools, and even ecologists (Drew Purves) to research far-field ideas like the relationship between ecology and …
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Posted: at 6:26 pm
When a technology hype flops, do you think the industry can use it as a learning experience? A time of self-examination? An opportunity to pause and reflect on making the next consumer or business tech hype a bit less stupid?
Don’t be silly.
What it does is pile the next hype on to the last hype, and call it “Hype 2.0”.
“With AI integration in wearables, we are entering ‘wearable 2.0’ era,” proclaim analysts Counterpoint Research in one of the most optimistic press releases we’ve seen in a while.
It’s certainly bullish for market growth, predicting that “AI-powered wearables will grow 376 per cent annually in 2017 to reach 60 million units.”
In fact it’s got a new name for these “hearables”. Apple will apparently have 78 per cent of this hearable market.
The justification for the claim is that language-processing assistants like Alexa will be integrated into more products. Counterpoint also includes Apple Airpods and Beats headphones as “AI-powered hearables”, which may be stretching things a little.
It almost seems rude to point out that the current wearables market a bloodbath for vendors is already largely “hearable”. Android Wear has been obeying OK Google commands spoken by users since it launched in 2014:
Apple built Siri into its Apple Watch in 2015 with its first update, watchOS 2:
Microsoft’s Band built in Cortana:
If a “smart” natural language interface had the potential to make wearables sell, surely we would know it by now. But we hardly need to tell you what sales of these devices are. Many vendors have hit paused, or canned their efforts completely. You could even argue that talking into a wearable may be one of the reasons why the wearable failed to be a compelling or successful consumer electronics story. People don’t want to do it.
Sprinkling the latest buzzword machine learning or AI over something that isn’t a success doesn’t suddenly make that thing a success. But AI has always had a cult-like quality to it: it’s magic, and fills a God-shaped hole. For 50 years, the divine promise of “intelligent machines” has periodically overcome people’s natural scepticism as they imagine a breakthrough is close at hand. Then it recedes into the labs again. All that won’t stop people wishing that this time AI has Lazarus-like powers.
We can’t wait for our machine-learning powered Sinclair C5 the Deluxe Edition with added Blockchain.