Category Archives: Artificial Intelligence

Hedge funds seeking artificial intelligence expertise need to cast the net wide these days, due to a shortage of people and a massive uptick in demand over the past 12 months.

Citadel has just turned to Microsoft for the new role of chief AI officer. Li Deng, who joined the tech firm straight out of academia 17 years ago, has just joined Citadels hedge fundoperation inSeattle, but will work also across Chicago and New York.

Deng announced his move to Citadel on LinkedIn yesterday, saying that he was very excited about the opportunities for artificial intelligence innovation here and the firms passion for growing its leadership in this space. Citadel didnt immediately respond to requests for comment.

Deng was chief scientist of AI and partner research manager at Microsoft. He joined in December 1999 from Waterloo University in Washington where he was a professor. He clearly has a passion for expanding AI knowledge he headed up Microsofts AI school, as well as founding its deep learning technology centre.

Citadel is the latest big buy-side firm to create a new role heading up AI and machine learning as hedge funds rely on ever-more complex datasets to gain an edge over the competition.

Man Group brought in William Ferreira as head of machine learning for its discretionary hedge fund business GLG in April. It was a newly-created role and he previously worked at Florin Court Capital. David Ferrucci, who previously headed up IBMs development of super-computer Watson, joined Bridgewater Associates in 2012 and now heads up its AI function, the Systematized Intelligence Lab, which has been growing this year

Hes kept his hand in academia, and was affiliate professor at the University of Washington for over 17 years until he joined Citadel in May. Hes written numerous books on using deep learning for automatic speech recognition as well as deep learning applications and methods.

Citadel already has a head of machine learning. Pradeep Natarajan joined from Amazon, where he was a senior research scientist, in October 2014.

Its also its second stab at poaching from Microsoft it brought in Kevin Turner, the tech firms ex-COO as CEO of Citadel Securities in August last year, but he left just seven months later. Hes now founder and CEO of his own start-up Forward Progress Ventures.

Contact:pclarke@efinancialcareers.com

Image: Getty Images

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Citadel has just hired a new head of artificial intelligence from Microsoft - eFinancialCareers

Financial Services compliance departments are constantly turning to technology to find efficiencies and satisfy increasingly tough regulatory examinations. It started with simple robotics, which can provide great operational efficiencies and help standardize processes. Never ones to rest on their laurels, compliance departments have begun looking to Artificial Intelligence (AI) as the next technological step to enhance and improve their programs. PayPal has cut its fraud false alerts in half by using an AI monitoring system that can identify benign reasons for seemingly bad behavior. HSBC recently announced a partnership to use AI in its Anti-Money Laundering (AML) program. Despite the adoption by some large players, there is still a lot of hesitancy and concern about the use of AI in financial crimes compliance.

WHAT IS AI AND HOW DOES IT WORK?

AI is computer software that can make decisions normally made by a human. What does this mean? In essence this means that it is computer software that can analyze large amounts of data and use patterns and connections within that data to reach certain results about that data.

Just like people, AI needs to learn in order to make decisions. It can do this in two ways: supervised or unsupervised learning. Supervised is the most common method, whereby data, the goal, and the expected output of that data are provided to the software allowing it to identify algorithms to get to the expected result. Supervised learning allows AI to use a feedback loop to further refine its intended task. If it identifies potential fraud, that turns out not to be, it can incorporate that feedback and uses it for future evaluation.

Unsupervised learning provides the software with only the data and the goal, but with no expected output. This is more complex and allows the AI to identify previously unknown results. As the software gets more data, it continues to refine its algorithm, becoming increasingly more efficient at its task.

HOW CAN IT HELP IN FINANCIAL CRIMES COMPLIANCE?

While there are varied uses in this space, one of the most relevant is to monitor transactions for potential criminal activity. Instead of using rule-based monitoring that looks for very specific red flag activity, AI software can use a large amount of data to filter out false alerts and identify complex criminal conduct. It can rule out false positives by identifying innocuous reasons for certain activity (investigation that normally needs to be done by an analyst) or see connections and patterns that are too complex to be picked up by straight forward rule-based monitoring. The reason it is able to do this is that AI software acts fluidly and can identify connections between data points that a human cannot. Its ability to analyze transactions for financial crime is only limited by the data available to it. Some specific uses are:

Fraud Identification: Identifying complex fraud patterns and cutting down on the number of false alerts by adding other data (geolocation tagging, IP addresses, phone numbers, usage patterns, etc.). See Paypals success in the first paragraph.

AML Transaction Monitoring and Sanctions Screening: Similar to fraud identification, it can greatly reduce the amount of false alerts by taking into account more data. It can also identify complex criminal activity occurring across products, lines of business, and customers.

Know Your Customer: Linkage detection between accounts, customers, and related parties to fully understand the risk of a party to the bank. Also, through analysis of unstructured data it can identify difficult to identify relevant negative news.

Anti-Bribery, Insider Trading, and Corruption: It can be used to identify insider trading or bribery by analyzing multiple source of information including emails, phone calls, messaging, expense reports, etc.

ANY CONCERNS?

Seems amazing, right? You might be wondering why everyone isnt immediately implementing these solutions throughout their financial crime compliance programs. While there have been some early adopters, there is still a lot of hesitation to use AI in the Financial Crime compliance space due to the highly regulated nature of the field. There is no doubt that AI will bring a huge lift in the future, but here are some of the concerns that need to be ironed out before we see large scale adoption:

Black box image of AI decisioning

By using more data than a human could synthesize, it may select patterns and results that wouldnt necessarily make sense to a person. As a result, AI providers need to ensure that AI derived decisions are supported by an auditable rationale that is clear to person. Clear documentation around how the AI gets to its results will be necessary.

Algorithmic Bias

Because AI software functions are based on the data it is provided, the impact of misinformation or biased information could be very large. This can occur when unintentional bias within the source data and training is uploaded into the algorithms the AI uses to perform its task. No one wants to end up with an AI transaction monitoring system that is flagging transactions based on racial or nationality bias.

Lack of regulatory acceptance

Currently, there appears to be a lack of regulatory acceptance mostly due to the first two concerns described above. That being said, in the United States, the Securities and Exchange Commission and the Financial Industry Regulatory Authority are both working on limited use of AI in their organizations. This is a strong step in having them able to understand and test it.

WHAT TO DO?

Now you know how AI can help your program and some of the concerns you need to be mindful of, but what now? Here are a couple of next steps you can take to successfully implement AI into your Financial Crime Compliance Program:

Lastly, knowledge is power. Keep researching and make sure you understand the reality of what AI can bring to the table for you and your program.

The rest is here:

Artificial Intelligence: The Next Step in Financial Crime Compliance Evolution - Finextra (blog)

Artificial intelligence could be the most revolutionary force affecting productivity in the United States economy, says the president of the Federal Reserve Bank of San Francisco.

"Everyone in Silicon Valley thinks statisticians are mis-measuring the productivity provided by the internet, but it's not that," says John C. Williams, on a trip to Sydney this week.

"Instead, the technologies that we now use and love mostly affect our consumption of leisure rather than affect our output in factories or offices."

Positive data showing the US economy is nearing full employment and that inflation is edging higher prompted the US central bank to recently raise interest rates for the second time in three months.

The US Fed also announced it will push ahead with plans to gradually shrink its $US4.5 trillion ($6 trillion) bond portfolio.

But wages and productivity growth remain stubbornly low, prompting the question: are economists mis-measuring the advent of the digital economy and the role of the internet in sophisticated labour markets?

The productivity gains from the inventions of electricity and the combustion engine had much more influence on humans' output capacity, says Mr Williams, and the only innovation in recent times that might rival those is artificial intelligence.

"AI is interesting because that says we could replace sophisticated human functions with computers," he told an audience at the University of Technology Sydney. "Potentially, that could be revolutionary in terms of our productivity."

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Productivity growth in the US has averaged 0.6 per cent over the last five years, down from 2.2 per cent during 1947-2007, according to JP Morgan data.

It's a problem affecting Australia as well, with the Reserve Bank of Australia also flagging the role of the internet in domestic productivity output.

Mr Williams also reiterated the US Federal Reserve's plan to "normalise" interest rate movements and said the US had reached a "turning point" in its transition from economic recovery to expansion.

"The more public understanding, the less chance that [our] actions will fuel unnecessarily volatility in the markets," said Mr Williams.

"Therefore, our process has been widely telegraphed and it will continue to be gradual, predictable and transparent, or in a word, boring,"

The pick-up in inflation and solid unemployment rate have solidified the US Federal Reserve's case for keeping the US economy expanding for as long as possible.

"Gradually raising interest rates to bring monetary policy back to normal helps The Fed keep the economy growing at a rate that can be sustained for a longer time," said Mr Williams.

"If we delay too long, the economy will eventually overheat, causing inflation or some other problem. At some point, that would put us in the position of having to quickly reverse course to slow the economy. That risks stalling the expansion and setting us back into recession."

While Mr Williams is not a member of the Federal Open Market Committee this year and does not vote on monetary policy directly, economists broadly agree he is a relatively good signal of future policy. He was the director of research at the San Francisco Fed when now-Fed chair Janet Yellen was president of the bank.

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Artificial intelligence could be the answer for productivity woes - The Sydney Morning Herald

This kind of A.I. is spreading to thousands of domains (not just loans), and as it does, it will eliminate many jobs. Bank tellers, customer service representatives, telemarketers, stock and bond traders, even paralegals and radiologists will gradually be replaced by such software. Over time this technology will come to control semiautonomous and autonomous hardware like self-driving cars and robots, displacing factory workers, construction workers, drivers, delivery workers and many others.

Unlike the Industrial Revolution and the computer revolution, the A.I. revolution is not taking certain jobs (artisans, personal assistants who use paper and typewriters) and replacing them with other jobs (assembly-line workers, personal assistants conversant with computers). Instead, it is poised to bring about a wide-scale decimation of jobs mostly lower-paying jobs, but some higher-paying ones, too.

This transformation will result in enormous profits for the companies that develop A.I., as well as for the companies that adopt it. Imagine how much money a company like Uber would make if it used only robot drivers. Imagine the profits if Apple could manufacture its products without human labor. Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. (As it happens, my venture capital firm has invested in just such a loan company.)

We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work. What is to be done?

Part of the answer will involve educating or retraining people in tasks A.I. tools arent good at. Artificial intelligence is poorly suited for jobs involving creativity, planning and cross-domain thinking for example, the work of a trial lawyer. But these skills are typically required by high-paying jobs that may be hard to retrain displaced workers to do. More promising are lower-paying jobs involving the people skills that A.I. lacks: social workers, bartenders, concierges professions requiring nuanced human interaction. But here, too, there is a problem: How many bartenders does a society really need?

The solution to the problem of mass unemployment, I suspect, will involve service jobs of love. These are jobs that A.I. cannot do, that society needs and that give people a sense of purpose. Examples include accompanying an older person to visit a doctor, mentoring at an orphanage and serving as a sponsor at Alcoholics Anonymous or, potentially soon, Virtual Reality Anonymous (for those addicted to their parallel lives in computer-generated simulations). The volunteer service jobs of today, in other words, may turn into the real jobs of the future.

Other volunteer jobs may be higher-paying and professional, such as compassionate medical service providers who serve as the human interface for A.I. programs that diagnose cancer. In all cases, people will be able to choose to work fewer hours than they do now.

Who will pay for these jobs? Here is where the enormous wealth concentrated in relatively few hands comes in. It strikes me as unavoidable that large chunks of the money created by A.I. will have to be transferred to those whose jobs have been displaced. This seems feasible only through Keynesian policies of increased government spending, presumably raised through taxation on wealthy companies.

As for what form that social welfare would take, I would argue for a conditional universal basic income: welfare offered to those who have a financial need, on the condition they either show an effort to receive training that would make them employable or commit to a certain number of hours of service of love voluntarism.

To fund this, tax rates will have to be high. The government will not only have to subsidize most peoples lives and work; it will also have to compensate for the loss of individual tax revenue previously collected from employed individuals.

This leads to the final and perhaps most consequential challenge of A.I. The Keynesian approach I have sketched out may be feasible in the United States and China, which will have enough successful A.I. businesses to fund welfare initiatives via taxes. But what about other countries?

They face two insurmountable problems. First, most of the money being made from artificial intelligence will go to the United States and China. A.I. is an industry in which strength begets strength: The more data you have, the better your product; the better your product, the more data you can collect; the more data you can collect, the more talent you can attract; the more talent you can attract, the better your product. Its a virtuous circle, and the United States and China have already amassed the talent, market share and data to set it in motion.

For example, the Chinese speech-recognition company iFlytek and several Chinese face-recognition companies such as Megvii and SenseTime have become industry leaders, as measured by market capitalization. The United States is spearheading the development of autonomous vehicles, led by companies like Google, Tesla and Uber. As for the consumer internet market, seven American or Chinese companies Google, Facebook, Microsoft, Amazon, Baidu, Alibaba and Tencent are making extensive use of A.I. and expanding operations to other countries, essentially owning those A.I. markets. It seems American businesses will dominate in developed markets and some developing markets, while Chinese companies will win in most developing markets.

The other challenge for many countries that are not China or the United States is that their populations are increasing, especially in the developing world. While a large, growing population can be an economic asset (as in China and India in recent decades), in the age of A.I. it will be an economic liability because it will comprise mostly displaced workers, not productive ones.

So if most countries will not be able to tax ultra-profitable A.I. companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their A.I. software China or the United States to essentially become that countrys economic dependent, taking in welfare subsidies in exchange for letting the parent nations A.I. companies continue to profit from the dependent countrys users. Such economic arrangements would reshape todays geopolitical alliances.

One way or another, we are going to have to start thinking about how to minimize the looming A.I.-fueled gap between the haves and the have-nots, both within and between nations. Or to put the matter more optimistically: A.I. is presenting us with an opportunity to rethink economic inequality on a global scale. These challenges are too far-ranging in their effects for any nation to isolate itself from the rest of the world.

Kai-Fu Lee is the chairman and chief executive of Sinovation Ventures, a venture capital firm, and the president of its Artificial Intelligence Institute.

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A version of this op-ed appears in print on June 25, 2017, on Page SR4 of the New York edition with the headline: The Real Threat of Artificial Intelligence.

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The Real Threat of Artificial Intelligence - New York Times

Peter Wallqvist: "It's a good trend that governments are brave enough to pull the trigger on things like this"

Phil Fisk. Set Design: Vicky Lees

The Serious Fraud Office (SFO) had a problem. Its investigation into corruption at Rolls-Royce was inching towards a conclusion, but four years of digging had produced 30 million documents. These needed to be sorted into "privileged" and "non-privileged", a legal requirement that involves paying junior barristers to do months of repetitive paperwork. "We needed a way that was faster," says Ben Denison, chief technology officer at the SFO. So, in January 2016, he started working with RAVN.

Pronounced "Raven", the London startup builds robots that sift and sort data, not only neatly presented material, but also unstructured documents. "Where someone has scanned 300 pages, it's not uncommon to put one page in upside down," says co-founder Peter Wallqvist. "We need to deal with that real world of messy datasets."

The two teams started to feed material from the Rolls-Royce case into the AI. By July they had a viable system, and with the agreement of lawyers on both sides, they set the robot to work. The barristers were wading through 3,000 documents a day. RAVN processed 600,000 daily, at a cost of 50,000 - with fewer errors than the lawyers. "It cut out 80 per cent of the work," says Denison. "It also saved us a lot of money." For Rolls-Royce, it had the opposite effect. In January 2017, the engineering company admitted to "vast, endemic" bribery and paid a 671 million fine. "It's hard to imagine a better outcome," says Wallqvist.

RAVN's co-founders - Jan Van Hoecke, Simon Pecovnik, Sjoerd Smeets and Wallqvist - met at Autonomy, the UK's first unicorn, where they worked on early versions of AI-powered database management. In 2010, the four left to launch RAVN. The self-funded firm now has 51 employees, revenues of 3 million and around 70 clients, mainly city law firms. BT, which signed a "very significant" deal, credits RAVN with annual savings of 100 million, due to automated checks that ensure contracts' accuracy.

Plus, of course, there's the SFO, which is using RAVN in increasingly clever ways. That means allowing it to make subjective judgements, including pointing investigators to data it thinks is relevant to a case. "This is potentially very valuable," says Denison.

Wallqvist believes the system can go even further and make not just assessments, but predictions. For example, by suggesting likely outcomes of mergers and acquisitions. "We've gone to the level of figuring out and structuring data," says Wallqvist. "Now we have the ability to surface that record of the past to predict the future." Today, Watson. Tomorrow, Holmes.

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Artificial intelligence is entering the justice system - Wired.co.uk

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Why it matters to you

AI promises to transform the world. Companies like this one will pave the way.

Hes been called one of the foremost thinkers on the topic of artificial intelligence, so its no surprise that Andrew Ng the cofounder of Coursera, the lead developer ofStanford Universitys main Massive Open Online Course (MOOC) platform, and the founder of the Google Brain project is starting another AI company of his own now that hes left Baidu. The resume of this impressive entrepreneur reads like a laundry list of some of the most impressive achievements in AI technology, and it seems safe to assume that Ngs newest venture, known only as deeplearning.ai, wont disappoint.

While Ng has founded and led many of his own projects in the past, he was most recently attached to another behemoth of a company Chinese web giant Baidu. There, Ng was chief scientist and headed the companys (what else) Artificial Intelligence Group, turning the Beijing-based giant into one of only a handful of companies in the world with expertise in each of the major AI categories: speech,natural language processing, computer vision, machine learning, and knowledge graph. Ngs team was also responsible for bringing two new business groups into the company autonomous driving and the DuerOS Conversational Computing platform.

Three months ago, Ng announced his departure from the company, noting in aMedium post, Baidus AI is incredibly strong, and the team is stacked up and down with talent; I am confident AI at Baidu will continue to flourish. After Baidu, I am excited to continue working toward the AI transformation of our society and the use of AI to make life better for everyone.

At the time, he told Forbes that his future plans were still in flux: I am looking into quite a few ideas in parallel, and exploring new AI businesses that I can build. One thing that excites me is finding ways to support the global AI community so that people everywhere can access the knowledge and tools that they need to make AI transformations.

And that may just be what deeplearning.ai is all about. In his Twitter announcement, Ng said only that he hoped the company would help many of you, and promised more announcements soon. Until then, well wait with bated breath.

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Artificial intelligence genius Andrew Ng has another AI project in the works - Digital Trends

Intelligent machines are taking over thousands of jobs, and being qualified is no longer enough to keep your job. Earlier this year, consulting firm McKinsey and Co. released a study that said 51% of all jobs could be automated in the next 20 years. Even specialized professions like medicine, law and banking are feeling the heat of Artificial Intelligence (AI). A few months ago, investment bank JP Morgan made the news by introducing intelligent machines to review financial deals that once kept employees busy for thousands of hours. Diagnostics and other decision-making skills previously thought of as the exclusive preserve of human beings, will soon be better handled by machines.

But Garry Kasparov has a different take on the issue. On 11 May 1997, Russian chess grandmaster Kasparov became the first world champion to be defeated by a machine. Yet in his new book Deep Thinking: Where Artificial Intelligence Ends And Human Creativity Begins, he is optimistic about the future of people with skills even as he concedes the inevitability of intelligent machines becoming more prominent. The sensation of being challenged, surpassed and possibly replaced by automaton, or an invisible algorithm, is becoming a standard part of our society, he writes. So while smarter computers are one key to success, doing a smarter job of humans and machines working together is far more important.

Is it possible to beat this threat of being displaced? Theres ample research and books on the subject, and here are some of the things they suggest you could do to robot-proof your career.

Build empathy

Employers want people who are empathetic and collaborative, who can guide relationships and work in teams. Because empathy is something that even intelligent machines are incapable of. Recognizing the importance of this skill is Geoff Colvin in his book Humans Are Underrated : What High Achievers Know That Brilliant Machines Never Will. The critical 21st century skill is empathy: we empathize to survive, he says, pointing to the healthcare profession. So while machines may be superior with diagnostics, a patient still needs to have a conversation with an expert. An empathetic doctor can help the patient deal with his condition better and recover faster. This, in turn, leads to lower healthcare costs and fewer lawsuits, says Colvin.

Empathy is a skill that can be developed through learning how to study the thoughts and feelings of others, and then responding appropriately. This involves inviting people to speak about their worries and concerns, hearing them out and then reassuring them, says Colvin.

Be a good communicator

A skill like communication is less easy to automate, says Anu Madgavkar, partner with McKinsey Global Institute, the research arm of McKinsey and Co., Mumbai. Intelligent machines cannot communicate the way human beings do. So people with better communication skills will be harder to replace with AI. The bigger message for professionals is that they should learn to communicate in a more compelling way, learn to work in teams, to excel at social interactions, says Madgavkar.

Become a lifelong learner

Previously in history, even in the 20th century, life was divided into two main parts: in the first part, you mostly learned, acquired knowledge and skills, and built yourself a personal and a professional identity. In the second part, you mostly made use of those skills and those identities. The pace of change in the 21st century will be such that most of what you learn as a teenager will be completely irrelevant by the time youre 40, says Yuval Noah Harari, author of Homo Deus: A Brief History Of Tomorrow, in a February interview with Time magazine, where he emphasized the necessity of life-long learning.

The good news is that anytime, anywhere learning is a reality now. For instance, if you want to do a project on design thinking, you can go immediately to the massive open online courses at online platforms like edX and Coursera and do a course on it, says Vijay Thadani, co-founder, NIIT.

Get those number skills

Digital literacy should be taken as seriously as language literacy, says Infosys chief executive Vishal Sikka, in an Infosys commissioned study on how to amplify human potential. The most important academic subjects that decision-makers see as focus areas for future generations are computer sciences, business and management and mathematics, says the study, which looked at the skills professionals need to acquire to integrate AI in a positive way into organizations and society.

Be constructive

Many perceive AI as a threat. Prominent among them are entrepreneur Elon Musk (our biggest existential threat) and scientist Stephen Hawking (the development of full AI could spell the end of the human race). From elevator operators to bank tellers and airplane pilots, history is full of examples of how technology has made jobs redundant.

But technology has also made life safer, easier and better. Its better to accept AI as a part of development, and look at the avenues it opens up rather than see the situation as man versus machine, says Kasparov.

Start to look at tasks hard to mechanizeanything that involves human creative energy, from photography and theatre, to baking, art, running, cooking classes, teachinganything thats not linear, says Mumbai-based Gurprriet Siingh, senior client partner at consulting firm Korn Ferry Hay Group. He says skills like empathy, creativity, flexibility and the ability to communicate can never be automated, and so education today should emphasize development of those skills.

Many of the most promising jobs today didnt even exist 20 years ago, says Kasparov, pointing to the demand for talent in new professions like app designers, 3D print engineers, drone pilots, social media managers and genetic counsellors. This is a trend that will accelerate as technology continues to create different professions .

Learn to work with machines

The future of increased productivity and business success isnt men or machines. Its both, argue Thomas H. Davenport and Julia Kirby in their book Only Humans Need Apply. Augment your skills, learn to work with machines, they say. The doctor who relies on diagnostic software, the lawyer who relies on research machines, the logistics manager who works with drones or the customer service manager who works with a chatbot, all of these professionals will be able to work better by complementing their human skills of empathy, of communication and creativity with machine intelligence. As the McKinsey report states, Humans will still be needed in the workforce; the total productivity gains we estimate will only come if people work alongside machines.

At wealth management firm ORO Wealth, for instance, the role of human portfolio advisers who work with intelligent machines is important. Even though the investment recommendations are machine-based, we need humans beings to work alongside. Because only a human adviser can empathize, can sense hesitation or lack of enthusiasm for a particular investment on the clients part. In which case they will go back to the machine-based algorithm, which will recommend alternative products, says Mumbai-based Vijay Kuppa, co-founder of ORO Wealth.

The skill and flexibility to work with a machine will help the workforce to become more productive. As Kasparov puts it, Smart machines will free us all...taking over the more menial aspects of cognition and elevating our mental lives towards creativity, curiosity, beauty and joy. These are what truly make us human, not any particular activity or skill like swinging a hammeror even playing chess.

First Published: Sun, Jun 25 2017. 03 47 PM IST

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Artificial Intelligence-proof your career - Livemint

Artificial intelligence has made great strides in the past few years, but its also generated much hype over its current capabilities.

Thats one takeaway from a Friday panel in San Francisco involving leading AI experts hosted by the Association for Computing Machinery for its 50th annual Turing Award for advancements in computer science.

Michael Jordan, a machine learning expert and computer science professor at University of California, Berkeley, said there is way too much hype regarding the capabilities of so-called chat bots. Many of these software programs use an AI technique called deep learning in which they are trained on massive amounts of conversation data so that they learn to interact with people.

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But despite several big tech companies and new startups promising powerful chat bots that speak like humans when prodded, Jordan believes the complexity of human language it too difficult for bots to master with modern techniques like deep learning. These bots essentially perform parlor tricks in which they respond with comments that are loosely related to a particular conversation, but they cant say anything true about the real world.

We are in era of enormous hype of deep learning, said Jordan. Deep learning has the potential to change the economy, he added, but we are not there yet."

Also in the panel, Fei-Fei Li, Googles ( goog ) machine learning cloud chief and Stanford University Professor, said We are living in one of the most exciting and hyped eras of AI. Li helped build the ImageNet computer-vision contest, which spurred a renaissance in AI in which researchers applied deep learning to identify objects like cats in photos.

But while everyone talks about ImageNets success, we hardly talk about the failures, she said, underscoring the hard work researchers have building powerful computers that can see like humans.

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Still, Li is excited that current AI milestones will eventually lead to more breakthroughs that will touch every single industry, like healthcare. We are entering a new phase in AI, she said.

What will help usher more breakthroughs in deep learning will be the continuing advancements in powerful computing hardware, like Nvidia's GPUs that make it possible to crunch tremendous amounts of data faster than ever, explained Ilya Sutskever, the research director of Elon Musk-backed AI research group OpenAI . Deep learning will keep booming in tandem with advancements in computing hardware that shows no signs of slowing down .

"Compute has been the oxygen of deep learning," Sutskever said.

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Beware the Hype of Artificial Intelligence - Fortune

Is AI over-hyped in 2017? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by Joanne Chen, Partner at Foundation Capital, on Quora:

To quote Bill Gates We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don't let yourself be lulled into inaction.

In short, over the next ten years, I dont believe AI will be overhyped. However, in 2017, will all of our jobs be automated away by bots? Unlikely. I believe the technology has incredible potential and will permeate across all aspects of our lives. But today, my sense is that many people dont understand what the state of AI is, and thus contribute to hype.

Artificial intelligence, a concept dating back to the 50s, is simply the notion that a machine can performance tasks that require human intelligence. But AI today is not what the science fiction movies portray it to be. What we can do today falls in the realm of narrow AI (vs general intelligence), which is the idea that machines can perform very specific tasks in a constrained environment. With narrow AI, there are a variety of techniques that you may have heard of. Ill use examples to illustrate differences.

Lets say you want to figure out my age (which is 31).

1) Functional programming: what we commonly know as programming, a way to tell a computer to do something in a deterministic fashion. I tell my computer that to compute my age, it needs to solve AGE = todays date birth date. Then I give it my birth date (Dec 4, 1985). There is 0% chance the computer will get my age wrong.

2) Machine learning: an application of AI where we give machines data and let them learn for themselves to probabilitically predict an outcome. The machine improves its ability to predict with experience and more relevant data. So take age for example. What if I had 1,000 data sets of peoples ages and song preferences? Song preference is highly correlated with generation. For example, Led Zeppelin and The Doors fans are mostly 40+ and Selena Gomez fans are generally younger than 25. Then I could ask the computer given that I love the Spice Girls and Backstreet Boys, how old does it think I am? The computer then looks at these correlations and compares it with a list of my favorite songs to predict my age within x% probability. This is a very simple example of using machine learning..

3) Deep Learning: is a type of machine learning emerged in the last few years, and talked widely about in the media when Google DeepMinds AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go.

Deep learning goes a step further than ML in that it enables the machine to learn purely by providing examples. In contrast, ML requires programmers to tell the computer what it should look for. As a result, deep learning functions much more like the human brain. This especially works well with applications like image recognition.

4) Deep reinforcement learning: DRL goes one step further and combines deep learning with reinforcement learning which is the notion of learning by trial-and-error, solely from rewards or punishments. DRL mimics how children learn they see observe other people doing things, they try things out and depending on the reward, they either repeat them or not!

Machine learning technologies have become more available (and the reason why there has been increasing media hype around this space) has been driven by advancements in three areas:

1) Infrastructure to run ML algorithms massive improvements in storage, processing capabilities (i.e. GPUs that speed up parallel processing), and accessibility for rapid innovation (cloud).

2) New available algorithms developed.

3) Data proliferation to train algorithms.

Between algorithms innovation and data availability, I believe data plays a more crucial role in advancements. If you look at the chart below, breakthroughs in AI have been quickly followed by availability of datasets, while many of the corresponding algorithms have been available for over a decade.

AI will permeate our lives in the next ten years. Think of the possible time, money, and manpower saved by automating simple processes. And as the technology becomes more advanced, the use cases will get even more exciting. I think its a wonderful time as an entrepreneur to be able to leverage this technology, and I couldnt be more excited as an investor.

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Is Artificial Intelligence Overhyped in 2017? - HuffPost

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer.

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer.

Theres no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai and others.

All these companies and researchers are convinced of the technologys enormous commercial potential. If AI develops like other technologies, most of these benefits will flow to the country that builds the first good ecosystem. This is a huge opportunity for Canada.

At the same time, AI poses clear challenges to business and government. Over the next 10 to 20 years, nearly half of Canadas jobs are at high risk of being affected by automation. Women hold a lot of these jobs and are especially at risk the World Economic Forum says that globally, women will face about twice the rate of job loss as men in what it calls the fourth industrial revolution.

How can Canadian companies gain the benefits of this disruptive technology while ensuring that large segments of society are not left behind? In our view, the public and private sectors should take six steps to outsmart AI and avoid its dislocations:

Commit to building the worlds best AI ecosystem: The winning AI cluster will create many high-paying jobs and create spillover effects for the middle class but the also-rans will not. Half-measures wont work. Canada must play to win. If there is going to be a steam engine that disrupts the status quo and AI is shaping up that way then Canada should develop and build the very best steam engine it can, right here at home.

Create at-scale AI training programs: Industry can form coalitions to collect data, oversee curriculum development and rapidly retrain workers in the skills needed to succeed in nascent AI applications.

Take Generation, a McKinsey-supported initiative that works with employers to quickly train and place young workers in sectors like health care and technology. Graduates have an 84 per cent employment rate within 90 days of completing the program and earn two to six times more income than before. Similarly, Prominp in Brazil trains 30,000 youth each year for positions in the oil and gas industry, with 189 skill-profile tracks and an 80-per-cent postgrad employment rate.

In Canada, such a program could be built in partnership with new research groups such as the Vector Institute in Toronto or with incubators such as Communitech, Next Canada and the Creative Destruction Lab.

Launch innovative new training models: The government could launch and fund a venture capital lab to create innovative training programs, so new training ideas can be tested, validated and scaled up (as recommended by the Advisory Council on Economic Growth). Startups such as Ryersons Magnet have great potential to address labour-market challenges. A so-called FutureSkills Lab could help scale great ideas and share learnings across provinces.

Build real links between companies and research schools: Large companies could partner with universities and vocational schools to provide equipment, facilities and expertise to prepare students for AI. In exchange, these companies could receive preferential recruiting.

For example, TAFE SA in South Australia trains approximately 500,000 students each year in high-demand areas such as aged care and nursing, trades and information technology. It partners with hundreds of businesses each year, which provide apprenticeships and traineeships. TAFE also orchestrates reverse co-op program where large corporations and small-to-medium-sized enterprises send workers back to campus for a term to learn critical AI skills.

Urgently reinvent curriculums for software and AI: Elementary, high-school and university programs have to develop the skills that empower students to be leaders in the coming AI tsunami critical thinking, teamwork, coding, algorithmic understanding and math. Some jurisdictions (e.g., Chicago and Queensland, Australia) are already moving to make software-coding classes mandatory. Canada should consider doing the same.

Government may want to consider practising what it preaches and adopt AI itself: A technology-enabled, AI-smart public service could not only be more efficient and provide better services. It might also create a product that Canada could export to the world.

Canadian companies have a real opportunity to leverage AI for growth but not without an inclusive work force. We all have a stake in getting this right.

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Canada has a chance to monopolize the artificial intelligence industry - The Globe and Mail