Category Archives: Artificial Intelligence

Frost & Sullivan anticipates that as the learning experience becomes increasingly digitised, technologies and solutions incorporating big data and analytics, collaboration, Augmented / Virtual Reality technology, Artificial Intelligence and learning management systems will play a key role within universities in the coming years.

Frost & Sullivan's most recent analysis, Australian Edutech Market: Key Trends, Technologies and Opportunities 2016-2022 finds that the Australian Edutech Market is expected to grow to AUD 1.7 Billion by 2022.

Big data and analytics will be a key method of engaging with students to deliver learning content personalisation, enhancement of student support services as well as providing insights into efficient campus management. This will be a significant area of growth in the Australian Education sector allowing specialised big data providers as well as integrators to reap the opportunities in this space. Whilst many universities use big data in small-scale applications, few have embarked on a single holistic campus-wide view of collecting and analysing data across devices, applications and networks.

"Predictive analytics will be a key area of future demand as academics and administrators place considerable value on the ability to proactively 'predict' outcomes rather than merely providing descriptive feedback in areas such as student performance and academic risk to enable course design and student support resources," noted Eran Halevi, Industry Analyst, Digital Transformation Practice, Frost & Sullivan Australia & New Zealand.

Artificial Intelligence is another technology sector that will see good growth in the next 10 years, noted Halevi.

"Across the education sector, early adoptions of AI have focused on assisting students with scheduling classes, timetables and administrative tasks. Future applications of AI may focus on highly-customised teaching, advanced research databases and greater predictive applications for student development. We are just beginning to see how the tertiary education sector will embrace cognitive services," she added.

Digital technologies are also starting to transform lecture theatres today. More students are using smart devices and online interactive services to access lecture courses and interact with various stakeholders within and outside the university. The rise of Massive Open Online Courses (MooCs) and Online Courses will be inevitable. The biggest challenge in the industry to date with MooCs has been around the monetisation of the platform. It is increasingly expected that more MooCs will charge for their courses.

"The ability to offer new digital methods of learning will be critical as the next generation of students will prefer learning through digital platforms. The shift towards online and digital platforms will see the rise of players that will disrupt the LMS, UC, Collaboration and conferencing segments in the years to come," noted Audrey William, head of research, Digital Transformation Practice, Frost & Sullivan Australia & New Zealand.

Frost & Sullivan's report, Australian Edutech Market, 2017, forms a part of the Frost & Sullivan Australian Digital Transformation Research program. All research services included in this subscription provide detailed market opportunities and industry trends evaluated following extensive interviews with market participants. For queries and more information please send an e-mail with your contact details to David Hymers, Frost & Sullivan Australia, at david.hymers@frost.com.

About Frost & Sullivan

Frost & Sullivan, the Growth Partnership Company, works in collaboration with clients to leverage visionary innovation that addresses the global challenges and related growth opportunities that will make or break today's market participants. For more than 50 years, we have been developing growth strategies for the global 1000, emerging businesses, the public sector and the investment community. Is your organization prepared for the next profound wave of industry convergence, disruptive technologies, increasing competitive intensity, Mega Trends, breakthrough best practices, changing customer dynamics and emerging economies? Contact us: Start the discussion

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Big Data & Analytics, Virtual and Augmented Reality, Artificial Intelligence and Cloud are driving universities to ... - PR Newswire (press...

How Sensors, Robotics And Artificial Intelligence Will Transform Agriculture Forbes The world population is expected to reach 9.7 billion by 2050. China and India, the two largest countries in the world, have populations totalling around one billion. In four years, by 2022, India is predicted to have the largest population in the ...

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How Sensors, Robotics And Artificial Intelligence Will Transform Agriculture - Forbes

International Business Machines (NYSE: IBM) has been betting big on artificial intelligence (AI). The company's AI-enabled Jeopardy!-winning cognitive supercomputer, Watson, has become the catch-all for the company's efforts in the area. Watson has been touted to revolutionize such diverse areas as cybersecurity, customer service, and even tax return preparation.

But nowhere is IBM's bet on Watson more evident than in the area of healthcare. The supercomputer's ability to analyze vast stores of data and recognize patterns make it a natural fit for medical applications.

IBM's tentpole program Watson for Oncology began in 2012 with a partnership with Memorial Sloan-Kettering Cancer Center doctors to tap their knowledge and catalog their specific expertise in rare forms of cancer. Those early collaborations produced impressive results and led to a full-court press to revolutionize healthcare. Watson is now addressing a variety of other medical areas including personalized care, patient engagement, imaging review, and drug discovery.

IBM acquired Truven to bolster Watson's medical credentials. Image source: IBM.

IBM has made numerous acquisitions in pursuit of its healthcare agenda. Late last year, the company spent $1 billion to acquire medical image company Merge Healthcare. The company's 30 billion images would be a key component in training Watson to identify abnormalities in X-rays and MRIs. This came on the heels of a $2.6 billion acquisition of Truven Health Analytics, which aggregated and analyzed data from more than 8,500 hospitals, insurers, and government agencies. IBM had previously acquired cloud-based data analytics company Explorys for its 50 million clinical data sets, as well as medical care solutions company Phytel. The total of these acquisitions is estimated at more than $4 billion to fund Watson's medical education.

Those investments appear to be paying off. Doctors at the University of North Carolina School of Medicine provided Watson with the records of 1,000 cancer patients, and it was able to provide treatment plans that concurred with oncologists' actual recommendations in 99% of cases. Additionally, Watson was able to provide additional options missed by its human counterparts in 30% of the cases, having been supplied with all the latest cancer research. This will provide effective cancer treatment to a wider variety of patients than ever before, while making every doctor with access to Watson a cancer expert.

Watson is making advances in the fight against cancer. Image source: IBM.

It is important to remember that all that glitters is not gold. IBM and Watson also partnered with the M.D. Anderson Cancer Center at the University of Texas back in 2012 to develop tools in the fight against cancer. The plan was to have Watson ingest medical literature, research data, and patient medical records, and with the use of AI, it would provide treatment recommendations and match patients with clinical trials.

In its highest profile misstep to date, IBM was forced to abandon the project late last year, while the cancer center's president resigned in disgrace. While audit reports suggest that project mismanagement was the culprit, it serves to illustrate that Watson can't fix everything.

IBM has been divesting itself from its legacy hardware, software, and services businesses, while transitioning to cloud computing, data analytics, and AI-based cognitive computing. These newer businesses, which it has dubbed "strategic imperatives", grew 13% in 2016 to accountfor 41% of total revenue, an indication that the transition is accelerating.

AI technology is being applied to a wide variety of industries, and new applications are being devised daily. IBM has focused on aggregating data and applying its cognitive chops and Watson's AI to helping find solutions for business, a different strategy from other companies in the field. This was a big gamble five years in the making, but as further advancements are being revealed, it appears IBM made the right call.

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IBM's Watson Is Tackling Healthcare With Artificial Intelligence - Madison.com

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People who apply for a loan from a bank or credit card company, and are turned down, are owed an explanation of why that happened. Its a good idea because it can help teach people how to repair their damaged credit and its a federal law, the Equal Credit Opportunity Act . Getting an answer wasnt much of a problem in years past, when humans made those decisions. But today, as artificial intelligence systems increasingly assist or replace people making credit decisions, getting those explanations has become much more difficult.

Traditionally, a loan officer who rejected an application could tell a would-be borrower there was a problem with their income level, or employment history, or whatever the issue was . But computerized systems that use complex machine learning models are difficult to explain, even for experts.

Consumer credit decisions are just one way this problem arises. Similar concerns exist in health care , online marketing and even criminal justice . My own interest in this area began when a research group I was part of discovered gender bias in how online ads were targeted , but could not explain why it happened.

All those industries, and many others, who use machine learning to analyze processes and make decisions have a little over a year to get a lot better at explaining how their systems work. In May 2018, the new European Union General Data Protection Regulation takes effect, including a section giving people a right to get an explanation for automated decisions that affect their lives. What shape should these explanations take, and can we actually provide them?

One way to describe why an automated decision came out the way it did is to identify the factors that were most influential in the decision. How much of a credit denial decision was because the applicant didnt make enough money, or because he had failed to repay loans in the past?

My research group at Carnegie Mellon University, including PhD student Shayak Sen and then-postdoc Yair Zick created a way to measure the relative influence of each factor. We call it the Quantitative Input Influence.

In addition to giving better understanding of an individual decision, the measurement can also shed light on a group of decisions: Did an algorithm deny credit primarily because of financial concerns, such as how much an applicant already owes on other debts? Or was the applicants ZIP code more important suggesting more basic demographics such as race might have come into play?

When a system makes decisions based on multiple factors it is important to identify which factors cause the decisions and their relative contribution.

For example, imagine a credit-decision system that takes just two inputs, an applicants debt-to-income ratio and her race, and has been shown to approve loans only for Caucasians. Knowing how much each factor contributed to the decision can help us understand whether its a legitimate system or whether its discriminating.

An explanation could just look at the inputs and the outcome and observe correlation non-Caucasians didnt get loans. But this explanation is too simplistic. Suppose the non-Caucasians who were denied loans also had much lower incomes than the Caucasians whose applications were successful. Then this explanation cannot tell us whether the applicants race or debt-to-income ratio caused the denials.

Our method can provide this information. Telling the difference means we can tease out whether the system is unjustly discriminating or looking at legitimate criteria, like applicants finances.

To measure the influence of race in a specific credit decision, we redo the application process, keeping the debt-to-income ratio the same but changing the race of the applicant. If changing the race does affect the outcome, we know race is a deciding factor. If not, we can conclude the algorithm is looking only at the financial information.

In addition to identifying factors that are causes, we can measure their relative causal influence on a decision. We do that by randomly varying the factor (e.g., race) and measuring how likely it is for the outcome to change. The higher the likelihood, the greater the influence of the factor.

Our method can also incorporate multiple factors that work together. Consider a decision system that grants credit to applicants who meet two of three criteria: credit score above 600, ownership of a car, and whether the applicant has fully repaid a home loan. Say an applicant, Alice, with a credit score of 730 and no car or home loan, is denied credit. She wonders whether her car ownership status or home loan repayment history is the principal reason.

An analogy can help explain how we analyze this situation. Consider a court where decisions are made by the majority vote of a panel of three judges, where one is a conservative, one a liberal and the third a swing vote, someone who might side with either of her colleagues. In a 2-1 conservative decision, the swing judge had a greater influence on the outcome than the liberal judge.

The factors in our credit example are like the three judges. The first judge commonly votes in favor of the loan, because many applicants have a high enough credit score. The second judge almost always votes against the loan because very few applicants have ever paid off a home. So the decision comes down to the swing judge, who in Alices case rejects the loan because she doesnt own a car.

We can do this reasoning precisely by using cooperative game theory , a system of analyzing more specifically how different factors contribute to a single outcome. In particular, we combine our measurements of relative causal influence with the Shapley value , which is a way to calculate how to attribute influence to multiple factors. Together, these form our Quantitative Input Influence measurement.

So far we have evaluated our methods on decision systems that we created by training common machine learning algorithms with real world data sets. Evaluating algorithms at work in the real world is a topic for future work.

Our method of analysis and explanation of how algorithms make decisions is most useful in settings where the factors are readily understood by humans such as debt-to-income ratio and other financial criteria.

However, explaining the decision-making process of more complex algorithms remains a significant challenge. Take, for example, an image recognition system, like ones that detect and track tumors . It is not very useful to explain a particular images evaluation based on individual pixels. Ideally, we would like an explanation that provides additional insight into the decision such as identifying specific tumor characteristics in the image. Indeed, designing explanations for such automated decision-making tasks is keeping many researchers busy .

Anupam Datta is Associate Professor of Computer Science and Electrical and Computer Engineering at Carnegie Mellon University

This article was originally published on The Conversation and was syndicated from TIME.com

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Did Artificial Intelligence Deny You Credit? - Fortune

Baidu (NASDAQ:BIDU) is one of the main companies that are trying to build a strong position in the promising market of artificial intelligence. In this article, I explain why I think investing in Baidu can be also a good way to gain exposure to AI. Artificial Intelligence offers a large number of functions that can strengthen Baidu's current businesses, but also gives the company the possiblity to exploit "new" markets such as autonomous driving and AI applications for financial services and healthcare.

The situation

I have been long Baidu more or less for 3 quarters but the stock hasn't gained much since I started my position. The market is still scared by the weak performance of the company's non-core divisions, while the recent need to adjust advertising practices has weighed on sales in the short-term. The core search market, despite a rise in revenue per customer, suffered from a reduction in the number of customers as a result of stricter regulations for online advertising.

While fundamentals remain very strong in the core search business, the market questions the company's ability to lead its non-core divisions to profitability.

The stock has gone nowhere for the last two years and while I believe it's one of the best large-caps stocks to hold for the long-term, I understand some of the market's doubt about Baidu's non-core divisions. ITE keeps reporting huge operating losses due to the rising content costs and the company's apparent desire to gain a scale advantage that could help them reinforce their leading position. I would love to see the division turn profitable, but I understand the strategy and I support it. As I wrote in a recent article:

I understand the company's investment in content for ITE, because I understand that a leading position in this business is the best factor for growth. The reason is that the economics of this business are basically the same of Pay-TV, which has already shown in many countries that the market leader has a clear advantage over the others and usually reports much better margins and a more stable growth than competitors. Trying to create a size advantage is probably the best strategy now.

On the other side, I am more disappointed by the results in the transaction services division, where I expected a faster growth and a constant decline in operating losses. Markets would love a sale of the division, but I think it's an unlikely scenario. Management has always considered Transaction Services as an integral and important part of the company's core business while CEO Robin Li once estimated the Chinese O2O market to be worth $1.6 trillion, which would mean the company is just starting to scratch the surface of this market.

The summary is that we have a strong core search business that will be back to growth soon, as the effects of the new regulation are mostly gone, while there are still some doubts on Baidu's non-core divisions, as the strong revenue growth is being constantly neutralized by rising operating expenses.

I am long because I think Baidu's growth prospects are still bright, despite the current losses in non-core divisions. After all, the company is a leader in basically all the three segments of Search, O2O services and subscription-based online videos. Baidu has still a lot of growth ahead, thanks to the positive trend in internet consumption in China.

Artificial Intelligence

One of the sectors where Baidu has been particularly active in the recent past is Artificial Intelligence. Artificial intelligence is one of the fastest-growing industries and expected to transform the way we conduct our lives and do business as the Industrial Revolution did in the 18th and 19th century. We know that AI functions are currently used in videogames, automatic language translators, self-driving cars, social networks and web-based advertisements, to name a few.

Baidu is also integrating AI in basically all of its businesses. Launched in September, Baidu's artificial intelligence platform "Baidu Brain" is already deployed across segments such as Search, News Feed, Maps, Nuomi, and PostBar, to name a few. As CEO Robin Li declared:

Users experience the magic of AI when they use Voice and Image Search or when we push targeted content in News Feed, recommend dishes in Nuomi, optimize a route in Maps, or intelligently suggest video content on PostBar. With AI, we are able to better match and predict user intent and deliver results that are more relevant and more targeted.

Besides those applications, AI can be employed in areas such as cloud, financial services and autonomous cars. Baidu's plan to become an important player in autonomous driving are known. The company is involved in a partnership with Nvidia (NASDAQ:NVDA) to develop a computing platform for self-driving cars and has recently announced it plans to start mass producing autonomous cars in five years.

Baidu has already started to test its vehicles in partnership with BMW (OTCPK:BMWYY) and Chinese automakers BYD, Chery and BAIC, although the partnership with BMW was terminated in November because "the development pace and the ideas of the two companies are a little different".

I think the company's prospects in the autonomous car market are bright. Baidu has a strong position in China and can leverage its knowledge in AI, machine learning and mapping to grow in the autonomous car market, which is particularly promising in the region. There's a large opportunity for autonomous vehicles in China, as the country has many cities, such as Beijing and Shanghai, where there is a high concentration of low- to middle-income individuals, who often are not wealthy enough to buy a car and would prefer to rely on an autonomous ride-hailing service for mobility. In 2015 , a World Economic Forum study found that 3 quarters of Chinese would be willing to use a self-driving car, suggesting a huge market for a ride-hailing service. The potential market size is huge, but we should keep our eyes on future developments, as competing in this industry against giants such as Apple and Alphabet won't be easy.

Autonomous driving is not the only "segment" where Baidu is trying to grow. More recently, Baidu started to use AI applications in the financial services market where AI is employed in risk-control techniques including facial and fingerprint recognition, liveliness detection and optical character recognition of identification documents.

AI applications are very useful in the healthcare sector as well. Back in October, the company launched a medical chatbot designed to make diagnosing illnesses easier. Ceo Robin Li said that AI is able to "redefine" the health care industry. He believes that artificial intelligence can be used in fields such as genetic testing or even in developing and testing of new medicines. There are many applications for AI in this space. For example, IBM Watson can analyze healthcare invoices and tell if a doctor, clinic or hospital makes mistakes repetitively in treating a condition, in order to help hospitals and doctors improve and avoid unnecessary hospitalizations of patients. Some more advanced "services" include those offered by Human Longevity, a young enterprise that offers its customers the possibility to complete genome sequencing, body scan and detailed check-ups in order to detect cancer or vascular diseases in their very early stage.

It's clear that AI can be a great thing for Baidu for two reasons:

1 - It can be easily integrated with Baidu's current businesses, improving the quality of its current services. For example, Baidu is using AI application to improve user experience in its search business, where voice-recognition can make online searches more practical. At the same time, customers are reached by targeted content in News Feed, by restaurant and retail suggestions in Nuomi, and by suggested video content on Postbar.

2 - AI offers Baidu the possibility to enter new markets, with autonomous driving being the main one. Autonomous driving is expected to be one of the main growth industries of the future. Estimates are very difficult at this stage and the institutions that tried to do so offered different scenarios. Nonetheless, they all forecast huge growth in the next 10-25 years. For example, the IEEE (Institute of Electrical and Electronics Engineers) estimated that up to 75% of cars in 2040 will be autonomous, while IHS automotive predicted that autonomous cars in 2035 will be 21 million. The Boston Consulting Group has more conservative expectations, forecasting that in 2035 there will be 18 million autonomous vehicles. In any case, the potential market size is huge.

The idea that investing in Baidu is a good way to bet on Artificial Intelligence is a result of a few considerations. As I said, the company can benefit from the growth in AI in two ways. It will have the possibility to exploit new markets such as autonomous driving and AI applications for financial services or healthcare. At the same time AI will strengthen its current business divisions by improving user experience. I see an investment in Baidu as a "conservative" bet on AI. Baidu already has good growth prospects in the core search business and the two underpenetrated markets of transaction services and subscription-based online videos, and AI applications can only strengthen those segments further. On the other side, the current investments in autonomous cars and other uncorrelated segments can be valuable options for long-term growth.

Thanks for taking the time to read the article. If you liked it, click on the follow button at the top of the page. You will get my articles as soon as they are published.

Disclosure: I am/we are long BIDU.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

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Investing In Baidu To Bet On Artificial Intelligence - Seeking Alpha

The idea of thinking machines may evoke thoughts of a Terminator-esque era, but the fact is that artificial intelligence, to an extent, is already a part of our lives and its presence is only set to grow. Artificial intelligence (AI) is a branch of computer science that deals with making computers simulate human intelligence. However technical and geeky that may sound, AI is a far less mundane technology than you might believe.

Image : shutterstock

Right from medical diagnoses to driverless cars, AI has fundamentally improved the way people consume a product, which is why marketers bet on it big time. A 2016 survey by Demandbase pointed out that over 80 percent of marketing executives believed that AI would revolutionise marketing by 2020. Here are a few ways in which AI could be leveraged in marketing to optimise customer experience:

Analytics: AIs indispensability in marketing stems from its abilities to spot trends from consumer-centric data. This data, when collated, can offer insights into consumer behaviour and help marketers predict future outcomes.

Voice recognition: With voice recognition, marketers are rapidly using chatbots to offer a more personalised treatment to their clientele. Depending on the mood of a customer, a voice recognition programme can change its tone to empathise and offer better insight into fixing an issue. Fast food restaurant chain Taco Bell came up with its own bot, TacoBot, to enable customers to place orders via instant messaging.

Predicting consumer behaviour: AI programmes can predict consumer behaviour details like the kind of products a consumer is interested in or the amount of time spent on reviewing a product. With research to back a marketing strategy, companies can offer tailor-made solutions to customers, thereby increasing chances of interactions. Video-streaming platform Netflix uses engagement data to recommend shows that a customer is likely to watch. Its algorithm analyses every repeat, click and pause to spot patterns from consumers watching history.

Digital Assistants: From Apple Siri to Microsofts Cortana to Amazons Echo devices, companies are rapidly integrating their products with AI to offer more personalised products. Backed with the ability to manage a host of interfaces, these digital assistants are all the rage for their multi-tasking abilities. So, the next time you want to buy a book online or orders some Chinese food, remember your digital assistant is just a shout away!

Automation: Automation remains an integral part of technical advances, highly revered for their ability cut down on costs and time consumed. AI- based programmes are the new buzz word for their operational efficiency. General Electric and Uber have jumped on the automation bandwagon for improved customer experiences, routing their cars, and the maintenance of equipment.

The hullabaloo about AI might seem a little surreal for some executives, but remember marketing was revolutionised by the internet and then social media, so computer systems simulating human intelligence might not be that far-fetched an idea. According to HubSpot founders Dharmesh Shah and Brian Halligan, smarter machines are likely to improve marketing software by acquiring the ability to do things without us explicitly telling them what to do.

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Artificial Intelligence: The next big thing in brand advertising - YourStory.com

Shared Prosperity Principle: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.

Income inequality is a well recognized problem. The gap between the rich and poor has grown over the last few decades, but it became increasingly pronounced after the 2008 financial crisis. While economists debate the extent to which technology plays a role in global inequality, most agree that tech advances have exacerbated the problem.

In an interview with the MIT Tech Review, economist Erik Brynjolfsson said, My reading of the data is that technology is the main driver of the recent increases in inequality. Its the biggest factor.

Which begs the question: what happens as automation and AI technologies become more advanced and capable?

Artificial intelligence can generate great value by providing services and creating products more efficiently than ever before. But many fear this will lead to an even greater disparity between the wealthy and the rest of the world.

AI expert Yoshua Bengio suggests that equality and ensuring a shared benefit from AI could be pivotal in the development of safe artificial intelligence. Bengio, a professor at the University of Montreal, explains, In a society where theres a lot of violence, a lot of inequality, [then] the risk of misusing AI or having people use it irresponsibly in general is much greater. Making AI beneficial for all is very central to the safety question.

In fact, when speaking with many AI experts across academia and industry, the consensus was unanimous: the development of AI cannot benefit only the few.

Its almost a moral principle that we should share benefits among more people in society, argued Bart Selman, a professor at Cornell University. I think its now down to eight people who have as much as half of humanity. These are incredible numbers, and of course if you look at that list its often technology pioneers that own that half. So we have to go into a mode where we are first educating the people about whats causing this inequality and acknowledging that technology is part of that cost, and then society has to decide how to proceed.

Guruduth Banavar, Vice President of IBM Research, agreed with the Shared Prosperity Principle, but said, It needs rephrasing. This is broader than AI work. Any AI prosperity should be available for the broad population. Everyone should benefit and everyone should find their lives changed for the better. This should apply to all technology nanotechnology, biotech it should all help to make life better. But Id write it as prosperity created by AI should be available as an opportunity to the broadest population.

Francesca Rossi, a research scientist at IBM, added, I think [this principle is] very important. And it also ties in with the general effort and commitment by IBM to work a lot on education and re-skilling people to be able to engage with the new technologies in the best way. In that way people will be more able to take advantage of all the potential benefits of AI technology. That also ties in with the impact of AI on the job market and all the other things that are being discussed. And they are very dear to IBM as well, in really helping people to benefit the most out of the AI technology and all the applications.

Meanwhile, Stanfords Stefano Ermon believes that research could help ensure greater equality. Its very important that we make sure that AI is really for everybodys benefit, he explained, that its not just going to be benefitting a small fraction of the worlds population, or just a few large corporations. And I think there is a lot that can be done by AI researchers just by working on very concrete research problems where AI can have a huge impact. Id really like to see more of that research work done.

AI is having incredible successes and becoming widely deployed. But this success also leads to a big challenge, said Dan Weld, a professor at the University of Washington. [That is] its impending potential to increase productivity to the point where many people may lose their jobs. As a result, AI is likely to dramatically increase income disparity, perhaps more so than other technologies that have come about recently. If a significant percentage of the populace loses employment, thats going to create severe problems, right? We need to be thinking about ways to cope with these issues, very seriously and soon.

Berkeley professor, Anca Dragan, summed up the problem when she asked, If all the resources are automated, then who actually controls the automation? Is it everyone or is it a few select people?

Im really concerned about AI worsening the effects and concentration of power and wealth that weve seen in the last 30 years, Bengio added.

Its a real fundamental problem facing our society today, which is the increasing inequality and the fact that prosperity is not being shared around, explained Toby Walsh, a professor at UNSW Australia.

This is fracturing our societies and we see this in many places, in Brexit, in Trump, Walsh continued. A lot of dissatisfaction within our societies. So its something that we really have to fundamentally address. But again, this doesnt seem to me something thats really particular to AI. I think really you could say this about most technologies. ... although AI is going to amplify some of these increasing inequalities. If it takes away peoples jobs and only leaves wealth in the hands of those people owning the robots, then thats going to exacerbate some trends that are already happening.

Kay Firth-Butterfield, the Executive Director of AI-Austin.org, also worries that AI could exacerbate an already tricky situation. AI is a technology with such great capacity to benefit all of humanity, she said, but also the chance of simply exacerbating the divides between the developed and developing world, and the haves and have nots in our society. To my mind that is unacceptable and so we need to ensure, as Elon Musk said, that AI is truly democratic and its benefits are available to all.

Given that all the jobs (physical and mental) will be gone, [shared prosperity] is the only chance we have to be provided for, added University of Louisville professor, Roman Yampolskiy.

Given current tech trends, is it reasonable to assume that AI will exacerbate todays inequality issues? Will this lead to increased AI safety risks? How can we change the societal mindset that currently discourages a greater sharing of wealth? Or is that even a change we should consider?

This article is part of a weekly series on the 23 Asilomar AI Principles.

The Principles offer a framework to help artificial intelligence benefit as many people as possible. But, as AI expert Toby Walsh said of the Principles, Of course, its just a start. a work in progress. The Principles represent the beginning of a conversation, and now we need to follow up with broad discussion about each individual principle. You can read the weekly discussions about previous principles here.

*The AI Arms Race Principle specifically addresses lethal autonomous weapons. Later in the series, well discuss the Race Avoidance Principle which will look at the risks of companies racing to creating AI technology.

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Artificial Intelligence And Income Inequality - Huffington Post

Meet Walter.

Im a huge fan of the original Alien franchise, largely because of Sigourney Weavers performance and the crucial part technology plays in its lore.

Not only does this tech allow humans to traverse immense distances to reach alien-infested worlds, but it provides them with androids: perfect robotic assistants so advanced that its extremely difficult to distinguish them from human crew. In fact, theyre superior to their human companions in many aspects, from their superhuman strength to their refined motor skills.

As a part of promoting the upcoming sci-fi horror film Alien: Covenant, (in theaters May 19), 21st Century Fox unit FOX, -0.40% Twentieth Century Fox released its branded short film Meet Walter, starring Michael Fassbender. It introduces Walter, the latest synthetic android, with intelligence powered by AMDs AMD, -2.36% Ryzen and Radeon processors and manufactured by the films fictional corporation, Weyland-Yutani.

This got me thinking. What really IS the future of AI? Where is AI now, and where is it heading? How close are we to having Walter-like androids help us with our daily chores? I spoke with Mark Papermaster, AMDs chief technology officer and senior vice president of technology and engineering, about these questions. This interview has been edited for length.

Q: What do you think about the modern AIs and their applications?

A: The overall field of machine learning, including AI, is taking a fascinating, but maybe not unexpected, direction: solving the worlds big problems. How do we get more people where they want to go safely with autonomous driving? How do we increase the throughput and reliability of our food supply chain with autonomous shipping? How can we make people healthier by analyzing medical problem sets so large that no human can reliably contemplate it? How do we better understand and improve our climate with planet-scale data analysis? AI may not be able to address every problem, but there are definitely immediate areas where we can put it to use.

There is so much data out there today, generated by the plethora of sensors and Interet-of-Things apps that pervade our work and homes. Over the next few years well see machine learning help us better understand all of this data, make it useful and then ultimately act on it in new and exciting ways.

Q: What are the biggest challenges AI faces?

A: Classification is where AI began. How do humans know that a rose is a plant, and a tree is a plant, but a tree is not a rose? We make these sorts of casual categorizations and relationships all the time, but teaching a computer program to do this quickly and automatically was challenging, but not impossible.

Then we taught computers to infer based on prior learnings, reach a conclusion and then act on it and then continue to repeat that cycle to achieve more intelligence.

Artificial Intelligence and Robotics Chair at Singularity University Neil Jacobstein talks about some recent achievements where AIs have been able to solve complex problems. He speaks with WSJ's Scott Austin at the CIO Network in San Francisco.

Now the real challenge is generating enough compute horsepower to do all of the calculations, training and inference so that a car can drive itself without human assistance, or we can even think about creating an entity as capable as Walter in the film. To reach this level, we need about 100 million times the compute acceleration than we have today.

If we are going to reach this goal, we must also begin to create AI systems that achieve reliable and useful results with the same kind of efficiency as the brain. When we look at a tree, we instinctively know that it is a tree without going through the approximately 100 billion calculations that a typical AI system does today to reach the same conclusion. When a human learns a new concept, it does so with increased efficiency of neural activity. Otherwise we would be constantly overwhelmed with data and computation clutter.

The last big challenge is how to achieve AI expertise. When humans learn to drive, expertise is improved with practice and exposure to a wide variety of scenarios that sharpen the skill level. In the same manner, we want AI systems to improve over time and experience.

Q: What issues with both software and hardware need to be resolved for AI to become closer to reaching the level of a perfect digital assistant, and then maybe a synthetic companion?

A: The Holy Grail of AI is, perhaps, a digital mind that functions like an organic one. In the near term, AI is focused on making constrained tasks much more productive, where theres a known set of inputs and a desired outcome.

Autonomous driving is a perfect example. There are a lot of variables to consider, how fast the car is going, how much distance between this car and the next, whats happening in the periphery, etc. In this instance, we are not replacing the human, we are assisting humans to help them have a more safe driving experience. To achieve a companion like Walter, we need massive amounts of compute power that dont exist today.

One the software side, developers will need to adapt and evolve software to take advantage of the compute power and architecture and features that will be developed. We are in the midst of a rapid evolution of the algorithms driving machine learning. New software frameworks are being developed to more easily utilize these algorithms. In tandem, the CPU, GPU, and specialized device compute chip capabilities are advancing enormously to meet the appetite of these algorithms to train more quickly, or infer results on the fly.

Can machines make art and music that moves us? Engineers and artists are testing that notion with an array of new artificial intelligence that is expanding the boundaries of how imagery, music and videogames are created. Image: Adele Morgan/The Wall Street Journal

Q: How invested in AI development is AMD? How are your processors specifically optimized for developing AI systems?

A: AMD has been focused on the compute engine aspects of machine learning. We are developing high-performance compute engines and enabling CPU and GPU processors to support the current and evolving AI algorithm models. To make application development efficient and more affordable, we are making the software enablement open source to facilitate the community at large to speed application development.

We are inspired by machine learning and see an infinite need for advancement. High-performance GPUs and CPUs have to evolve in sync with the rapid advance in machine-learning technology. It is critical that these platforms provide both the performance and the efficiency for a wide range of applications.

To begin to address early machine-learning projects, we rolled out our Radeon Instinct product line at the end of 2016. With machine learning, the system is trained using large amounts of data using computationally intensive algorithms. The high computational capacity of AMD GPUs make it a great match for machine learning during the processing of large amounts of data to train neural networks. The AMD Radeon MI25 accelerator will be based on our latest graphics architecture Vega, expected to come to market later this year.

We are targeting high-memory bandwidth and large addressable memory capacity, as well as high-throughput core performance with our upcoming Naples CPUs making the new products well suited for the deployment of machine learning and can be easily configured with Radeon Instinct Graphics compute or FPGA programmable devices.

Software is the other part of this equation and in order for it to advance as quickly as the hardware, you need an open source, industry standards-based development environment. Weve given developers more access to our GPU hardware than ever before with our GPUOpen initiative, and we have the Radeon Open Compute software platform to accelerate machine learning, and deep learning frameworks and applications.

Q: What potential applications of AI systems does AMD envision, and do they play a role in the companys business strategies?

A: AI is now in the process of mainstreaming, which means it is becoming easier to leverage AI into more and more applications. Everywhere a business has decisions that can be made by extensive analysis of data to get a known set of desired outcomes or optimizations can now be accelerated by AI algorithms. Like other companies, we will explore areas where we can use AI applications to benefit our business operations and pursue them if they make sense.

In addition, there are many applications in which the promise of AI value is still emerging but not validated yet. We will work with customers and researchers to bring useful solutions to these emerging application areas.

What lies ahead for the human species? Yuval Harari, author of "Homo Deus: A Brief History of Tomorrow," explores potential threats to the human race, as well as the possibility of immortality. In a follow-up to his best-seller, "Sapiens," he and WSJ's Tanya Rivero also discuss the questions posed by the rise of artificial intelligence. Photo: Getty

Q: You mentioned machine learning. Will androids think like humans do?

A: Thinking is the fountain from which all personality springs! Humans are guided by conscious thoughts, unconscious thoughts, learned behaviors, instinct, memories, and more but its all some form of thought. So one imagines that thinking is so simple, yet it is quite extraordinary.

CPUs wont replace that human element but machine learning can be incredibly effective in handling constrained situations, and learned tasks, tapping into massive stores of data and information to optimize specific decisions.

Q: Walter is a complex robotic unit, paired with equally complex AI, built to serve and function as a perfect companion. If the technology used to build his body was available today, and if AI was developed enough right now, would the current generation of AMDs processing units be able to provide enough processing power to make Walter as functional as advertised on the meetwalter.com website? If not, what would it take to get there?

A: Im reminded of an experiment conducted on the Fujitsu K Computer in 2013. That computer simulated 1.73 billion virtual nerve cells connected by 10.4 trillion simulated synapses. It took 82,944 CPUs to do this. More importantly, it took a full 40 minutes to simulate just one second of what the human mind is doing at any one time. So thats where the world is at today: warehouse-scale supercomputers are 2,400 times slower than the human mind.

At AMD we certainly see opportunities to speed that up with programmable CPU, as well as Graphics microprocessors, like Radeon Instinct, which optimize key aspects of the parallel thinking a human mind might do. Even so, the road to Walter is a long one.

Q: What kind of fail-safes would need to exist in his code and CPU to make Walter safe for humans? What needs to be done to prevent him from getting hacked or turning hostile?

A: This question highlights one of the biggest impediments to wide adoption of AI applications ensuring there are protections that prevent safety or ethical issues. The first proving ground will be autonomous driving applications, which will require safeguards that could then be applied to other machine-learning applications.

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Opinion: Will artificial intelligence deliver an android that works as your personal assistant? - MarketWatch

On a chilly March afternoon last year in the South Korean capital Seoul, a computer algorithm made history.

A program called AlphaGo beat the reigning human world champion at go, an ancient Chinese board game considered to be one of the most complex pastimes man has ever devised.

The game has remained an inviolably human pursuit for centuries, and one of the hardest challenges for artificial intelligence (AI) because of the vast number of possible moves more than the number of atoms in the universe and the need to employ creativity to win.

In Seouls Four Seasons hotel, AlphaGos victory over five games was ruthless: Lee Sedol, the 33-year-old human go grandmaster, lost 4-1. At a press conference afterwards, he said with a trace of wonder: Today, I am speechless.

Just two months earlier, AlphaGo had been featured on the cover of Nature, the premier peer-reviewed scientific journal, having defeated the human European go champion 5-0. That and its triumph over Lee cemented its position as a rare scientific breakthrough that came years ahead of scientists predictions.

This is the first time that a computer program has defeated a human professional player in the full-sized game of go, a feat previously thought to be at least a decade away, the team behind it wrote.

AlphaGo is the brainchild of DeepMind Technologies, a London-based AI company acquired by Google in 2014 for 400m. The AlphaGo feature was the second time in a year DeepMind had made the cover of Nature. Ten months later, last October, the team made a third appearance in the journal, making them singularly prolific among their academic peers.

With its cadre of researchers, from Bayesian mathematicians to cognitive neuroscientists, statisticians and computer scientists, DeepMind has amassed arguably the most formidable community of world-leading academics specialising in machine intelligence anywhere in the world.

What we are trying to do is a unique cultural hybrid the focus and energy you get from start-ups with the kind of blue-sky thinking you get from academia, says Demis Hassabis, co-founder and chief executive. Weve hired 250 of the worlds best scientists, so obviously theyre here to let their creativity run riot, and we try and create an environment thats perfect for that.

We learn about our algorithms by testing them on real-world, messy data sets

DeepMinds researchers have in common a clearly defined if lofty mission: to crack human intelligence and recreate it artificially.

The undertaking is one that 40-year-old Hassabis has been pondering ever since he became a professional chess master at 13 and the world number two in his age group. Playing chess at that young age got me thinking, how does the brain come up with moves and how do you make plans? he says. I got my first computer when I was eight. I bought it with winnings from chess competitions. One of the first big programs I wrote when I was 11 was an AI to play Othello. It wasnt particularly good, but it could give someone a game.

Today, the goal is not just to create a powerful AI to play games better than a human professional, but to use that knowledge for large-scale social impact, says DeepMinds other co-founder, Mustafa Suleyman, a former conflict-resolution negotiator at the UN.

The line might sound insincere if it came from an executive in Silicon Valley, where practically every start-up believes it is about to change the world. DeepMind, however, might actually be understating the sea-changes it is driving: its scientific advances are already employed in complex real-world scenarios that require pattern recognition, long-term planning and decision-making.

AlphaGo-like algorithms are, for example, being used to study protein-folding to speed up new drug discoveries at the UKs Crick Institute; to analyse medical images to allow sharper cancer diagnoses and treatment plans at Londons University College Hospital; and to save enormous amounts of energy in power-hungry data centres at Google. In the last of these, DeepMinds experiment resulted in energy savings of 15 per cent or 40 per cent of cooling energy translating to millions of dollars. The company now hopes to expand its range of clients to the UKs National Grid and other utilities providers.

We learn so much about the strength and weaknesses of our algorithms by testing them on large-scale, real-world, noisy and messy data sets, says Suleyman. Its a pretty unique way to make progress with our toughest social problems.

To solve seemingly intractable problems in healthcare, scientific research or energy, it is not enough just to assemble scores of scientists in a building; they have to be untethered from the mundanities of a regular job funding, administration, short-term deadlines and left to experiment freely and without fear.

If you look at how Google worked five or six years ago, [its research] was very product-related and relatively short-term, and it was considered to be a strength, Hassabis says. [But] if youre interested in advancing the research as fast as possible, then you need to give [scientists] the space to make the decisions based on what they think is right for research, not for whatever kind of product demand has just come in.

DeepMinds three appearances in quick succession in Nature, along with more than 120 papers published and presented at cutting-edge scientific conferences, are a mark of its prodigious scientific productivity. It is also an indication of its special status at Google.

Our research team today is insulated from any short-term pushes or pulls, whether it be internally at Google or externally. We want to have a big impact on the world, but our research has to be protected, Hassabis says. We showed that you can make a lot of advances using this kind of culture. I think Google took notice of that and theyre shifting more towards this kind of longer-term research.

DeepMind has six more early manuscripts that it hopes will be published by Nature, or by that other most highly regarded scientific journal, Science, within the next year. We may publish better than most academic labs, but our aim is not to produce a Nature paper, Hassabis says. We concentrate on cracking very specific problems. What I tell people here is that it should be a natural side-effect of doing great science.

Structurally, DeepMinds researchers are organised into four main groups with titles such as Neuroscience or Frontiers (a group comprising mostly physicists and mathematicians who test the most futuristic theories in AI). Beyond these are several smaller teams with deeper specialities. Many of the project managers are former video game producers who joined from Hassabiss previous company, Elixir Studios, an independent games developer.

Every eight weeks, scientists present what they have achieved to team leaders, including Hassabis and Shane Legg, head of research, who decide how to allocate resources to the dozens of projects. Its sort of a bubbling cauldron of ideas, and exploration, and testing things out, and finding out what seems to be working and why or why not, Legg says.

Projects that are progressing rapidly are allocated more manpower and time, while others may be closed down, all in a matter of weeks. In academia youd have to wait for a couple of years for a new grant cycle, but we can be very quick about switching resources, Hassabis says.

We want to have a big impact on the world, but our research has to be protected

At any point in time, the company also has two or three special forces-style units called strike teams that are formed temporarily to achieve a particular goal. This is what we did with AlphaGo. Once it started showing promise in the first six months, we put a large team of 15 people with specialised skills on it, to push that to the end, Hassabis says. It allows us to pick exactly the right specialists to make the perfect complementary team without being beholden to traditional reporting lines. So, theyre like on secondment to that project, and then they go back to their original teams.

This organisational culture has been a magnet for some of the worlds brightest minds. Jane Wang, a cognitive neuroscientist at DeepMind, used to be a postdoctoral researcher at Northwestern University in Chicago, and says that she was attracted to DeepMinds clear, social mission. I have interviewed at other industry labs, but DeepMind is different in that there isnt pressure to patent or come up with products there is no issue with the bottom line. The mission here is about being curious, she says.

For Matt Botvinick, neuroscience team lead, joining DeepMind was not just a career choice but a lifestyle change too. The former professor who led Princeton Universitys Neuroscience Institute continues to live in the US, where his wife is a practising physician, and commutes to DeepMinds labs in London every other week.

At Princeton, I was surrounded by people I considered utterly brilliant and had no interest in working in an environment any less focused on primary scientific questions, he says. But I couldnt resist the opportunity to come here because there is something qualitatively new going on, both with the scale and the spirit of ideas.

What sets DeepMind apart from academic labs, he says, is its culture of cross-disciplinary collaboration, reflected in the companys hiring of experts, who can cut across different domains from psychology to deep learning, physics or computer programming.

In a lot of research institutions, things can become siloed. Two neighbouring labs could be working on similar topics but never exchange and pool information, Botvinick says. Unlike any place Ive ever experienced before, all conversations are enhanced rather than undermined by differences in background.

Illustration by Scott Chambers

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DeepMind's social agenda plays to its AI strengths - Financial Times

Shawn Rickenbacker teaches Humans + Machines, a Social Innovation and Social Entrepreneurship course. An architect, he is a Taylor Senior Fellow and Favrot Visiting Chair. (Photo by Paula Burch-Celentano)

Artificial intelligence is at mostpeoples fingertips everyday. But we may not understand its implications and complexiites.

When you speak to Apples Siri or Amazons Alexa to retrieve info, or use Facebook, youre actually engaging with artificial intelligence, said Shawn Rickenbacker, a Taylor Senior Fellowand Favrot Visiting Chair in the Tulane School of Architecture.

Artificial intelligence (AI)is technology that simulates human cognitive functions for problem-solving, according to Rickenbacker.

Were engaging new technologies as an increasingly important complement to design thinking when innovating to solve complex problems.

Shawn Rickenbacker, Taylor Senior Fellow and Favrot Visiting Chair

He is teaching a course Humans + Machines: The Future Social Impact of Artificial Intelligence this semester to explore the complexities of human interaction with artificial intelligence. It is a Social Innovation and Social Entrepreneurship (SISE) course.

Rickenbackers goal is to introduce a new line of systems thinking across building and environmental issues.

Hes collaborating with associate professors of computer science Brent Venable and Carola Wenk to bring awareness to how various AI models learn and operate. Students are encouraged to identifyemerging trends in AI.

The class examines real-world AI models such as targeted Facebook ads, said Rickenbacker. Students also study associated data decision trees to understand algorithms methodology.

The class takes a cross-disciplinary approach. Were engaging in technological issues and using design thinking to tackle problems, he said. Diverse academic backgrounds such as economics, health sciences, psychology and architecture all contribute to the process.

Working in teams, students examine case studies and conduct real-time testing to investigate how AI can be designed to avoid unintended consequences andharmful bias and further enhance equity and fairness.

Rickenbacker is an architect and creative technologist, whose work has been featured in The New York Times, CNN International and Global Architecture. He is co-founder of Urban Data + Design, a research and design consultancy that focuses on the convergence and impacts of digital information on physical space.

Rickenbacker also is teaching an upper-level and graduate design studio course in which students use data to create urban systems and architecture to address air pollution and climate change in New York City.

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Students explore the social impact of artificial intelligence | Tulane ... - News from Tulane