Monthly Archives: July 2017

Earlier this week, at the campus of MIT, TechCrunch had the chance to sit down with famed roboticist Rodney Brooks, the founding director of MITs Computer Science and Artificial Intelligence Lab, and the cofounder of both iRobot and Rethink Robotics.

Brooks had a lot to say about AI, including his overarching concern that many people including renowned AI alarmist Elon Musk get it very wrong, in his view.

Brooks also warned that despite investors fascination with robotics right now, many VCs may underestimate how long these companies will take to build a potential problem for founders down the road.

Our chat, edited for length, follows.

TC: You started iRobot when there was no venture funding, back in 1990. You started Rethink in 2008, when there was funding but not a lot of interest in robotics. Now, there are both, which seemingly makes it a better time to start a robotics company. Is it?

RB: A lot of Silicon Valley and Boston VCs sort of fall over themselves about how theyre funding robotics [now], so you [as a founder] can get heard.

Despite [investors who say there is plenty of later-stage funding for robotics] , I think its hard for VCs to understand how long these far-out robotics systems will really take to get to where they can get a return on their investment, and I think thatll be crunch time for some founders.

TC: Theres also more competition and more patents that have been awarded, and a handful of companies have most of the worlds data. Does that make them insurmountable?

RB: Someone starting a robotics company today should be thinking that maybe at some point, in order to grow, theyre going to have to get bought by a large company that has the deep pockets to push it further. The ecosystem would still use the VC funding to prune out the good ideas from the bad ideas, but going all the way to an IPO may be hard.

Second thing: On this data, yes, machine learning is fantastic, it can do a lot, but there are a lot of things that need to be solved that are not just purely software; some of the big innovations [right now] have been new sorts of electric motors and controls systems and gear boxes.

TC: Youre writing a book on AI, so I have to ask you: Elon Musk expressed again this past weekend that AI is an existential threat. Agree? Disagree?

RB: There are quite a few people out there whove said that AI is an existential threat: Stephen Hawking, astronomer Royal Martin Rees, who has written a book about it, and they share a common thread, in that: they dont work in AI themselves. For those who do work in AI, we know how hard it is to get anything to actually work through product level.

Heres the reason that people including Elon make this mistake. When we see a person performing a task very well, we understand the competence [involved]. And I think they apply the same model to machine learning. [But they shouldnt.] When people saw DeepMinds AlphaGo beat the Korean champion and then beat the Chinese Go champion, they thought, Oh my god, this machine is so smart, it can do just about anything! But I was at DeepMind in London about three weeks ago and [they admitted that things could easily have gone very wrong].

TC: But Musks point isnt that its smart but that its going to be smart, and we need to regulate it now.

RB: So youre going to regulate now. If youre going to have a regulation now, either it applies to something and changes something in the world, or it doesnt apply to anything. If it doesnt apply to anything, what the hell do you have the regulation for? Tell me, what behavior do you want to change, Elon? By the way, lets talk about regulation on self-driving Teslas, because thats a real issue.

TC:Youve raised interesting points about this in your writings, noting that the biggest worry about autonomous cars whether theyll have to choose between driving into a gaggle of baby strollers versus a group of elderly women is absurd, considering how often that particular scenario happens today.

RB:There are some ethical questions that I think will slow down the adoption of cars. I live just a few blocks [from MIT]. And three times in the last three weeks, I have followed every sign and found myself at a point where I can either stop and wait for six hours, or drive the wrong way down a one-way street. Should autonomous cars be able to decide to drive the wrong way down a one-way street if theyre stuck? What if a 14-year-old riding in an Uber tries to override it, telling it to go down that one-way street? Should a 14-year-old be allowed to drive the car by voice? There will be a whole set of regulations that were going to have to have, that people havent even begun to think about, to address very practical issues.

TC: You obviously think robots are very complementary to humans, though there will be job displacement.

RB:Yes, theres no doubt and it will be difficult for the people who are being displaced. I think the role in factories, for instance, will shift from people doing manual work to people supervising. We have a tradition in manufacturing equipment that it has horrible user interfaces and its hard and you have to take courses, whereas in consumer electronics [as with smart phones], we have made the machines we use teach the people how to use them. And I do think we need to change our attitude in industrial equipment and other sorts of equipment, to make the machines teach the people how to use them.

TC: But do we run the risk of not taking this displacement seriously enough? Isnt the reason we have our current administration because we arent thinking enough about the people who will be impacted, particularly in the middle of the country?

RB: Theres a sign that maybe I should have seen and didnt. When I started Rethink Robotics, it was called Heartland Robotics. Id just come off six years of being an adviser to the CEO of John Deere; Id visited every John Deere factory. I could see the aging population. I could see they couldnt get workers to replace the aging population. So I started Heartland Robotics to build robotics to help the heartland.

Its no longer called Heartland Robotics because I started to get comments like, Why didnt you just come out and call it Bible Belt Robotics? The people in the Midwest thought we were making fun of them. I should have now, in retrospect, thought of that a little deeper.

TC: If you hadnt started Rethink, what else would you want to be focused on right now?

RB: Im a robotics guy, so every problem I think I can solve has a robotics solution. But what are the sorts of things that are important to humankind, which the current model of either large companies investing in or VCs investing in, arent going to solve? For instance: plastics in the ocean. Its getting worse; its contaminating our food chain. But its the problem of the commons. Who is going to fund a startup company to get rid of plastics in the ocean? Whos going to fund that, because whos going to [provide a return for those investors] down the line?

So Im more interested in finding places where robotics can help the world but theres no way currently of getting the research or the applications funded.

TC: Youre thought as the father of modern robotics. Do you feel like you have to be out there, evangelizing on the part of robotics and roboticists, so people understand the benefits, rather than focus on potential dangers?

RB: Its why Im right now writing a book on AI and robotics and the future because people are getting too scared about the wrong things and not thinking enough about what the real implications will be.

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This famous roboticist doesn't think Elon Musk understands AI - TechCrunch

The UKs upper house of parliament is asking for contributions to an enquiry into the socioeconomic and ethical impacts of artificial intelligence technology.

Among the questions the House of Lords committee will consider as part of the enquiry are:

The committee says it is looking for pragmatic solutions to the issues presented, and questions raised by the development and use of artificial intelligence in the present and the future.

Commenting in a statement, Lord Clement-Jones, chairman of the Select Committee on Artificial Intelligence, said: This inquiry comes at a time when artificial intelligence is increasingly seizing the attention of industry, policymakers and the general public. The Committee wants to use this inquiry to understand what opportunities exist for society in the development and use of artificial intelligence, as well as what risks there might be.

We are looking to be pragmatic in our approach, and want to make sure our recommendations to government and others will be practical and sensible. There are significant questions to address relevant to both the present and the future, and we want to help inform the answers to them. To do this, we need the help of the widest range of people and organisations.

If you are interested in artificial intelligence and any of its aspects, we want to hear from you. If you are interested in public policy, we want to hear from you. If you are interested in any of the issues raised by our call for evidence, we want to hear from you, he added.

The committees call for evidence can be found here. Written submissions can be submitted via this webform on the committees webpage.

The deadline for submissions to the enquiry is September 6, 2017.

Concern over the societal impacts of AI has been rising up the political agenda in recent times, with another committee of UK MPs warning last fall the government needs to take proactive steps tominimise bias being accidentally built into AI systems and ensure transparency so that autonomous decisions can be audited and systems vettedto ensure AI tech is operating as intended and that unwanted, or unpredictable, behaviours are not produced.

Another issue that weve flaggedhere on TechCrunch is the risk of valuable publicly funded data-sets effectively being asset-stripped by tech giants hungry for data to feed and foster commercial AI models.

Since 2015, for example, Google-owned DeepMind has been forging a series of data-sharing partnerships with National Health Service Trusts in the UK which has provided it withaccess to millions of citizens medical information. Some of these partnerships explicitly involve AI; in other cases it has started by building clinical task management apps yet applying AI to the same health data-sets is a stated, near-termambition.

It alsorecently emergedthat DeepMind is not charging NHS Trusts for the app development and research work its doing with them rather its price appears to be access to what are clearly highly sensitive (and publicly funded) data-sets.

This is concerning as there are clearly only a handful of companies with deep enough pockets to effectively buy access to highly sensitive publicly-funded data-sets i.e. by offering five years of free work in exchange for access using that data to develop a new generation of AI-powered products. A small startup cannot hope to compete on the same terms as the Alphabet-Google behemoth.

The risk ofdata-based monopolies and winner-takes-all economics from big techs big data push to garner AI advantage should be loud and clear. As should the pressing need for public debate on how best to regulate this emerging sector so that future wealth and any benefits derived from the power of AI technologies can be widely distributed, rather than simply locking in platform power.

In another twist pertaining to DeepMind Healths activity in the UK, the countrys data protection watchdog ruled earlier this month that the companys first data-sharing arrangement with an NHS Trust broke UK privacy law. Patients consent had not been sought nor obtained for the sharing of some 1.6 million medical records for the purpose of co-developing a clinical task management app to provide alerts of the risk of a patient developing a kidney condition.

The Royal Free NHS Trust now has three monthsto change how it works with DeepMind to bring the arrangement into compliance with UK data protection law.

In that instance the app in question does not involve DeepMind applying any AI. However, in January 2016, the company and the same Trust agreed on wider ambitions to apply AI to medical data sets within five years. So the NHS app development freebies that DeepMind Health is engaged with now are clearly paving the way for a broad AI push down the line.

Commenting on the Lords enquiry, Sam Smith, coordinator of health data privacy group, medConfidential an early critic of how DeepMind was being handed NHS patient data told us: This inquiry is important, especially given the unlawful behaviour weve seen from DeepMinds misuse of NHS data. AI is slightly different, but the rules still apply, and this expert scrutiny in the public domain will move the debate forward.

Link:

AI data-monopoly risks to be probed by UK parliamentarians - TechCrunch

After years of near-silence, Apple is slowly starting to make a bit of noise about its work on artificial intelligence. Last December the iPhone maker shared its first public research paper on the topic; this June it announced new tools to speed up machine learning on the iPhone; and today it started blogging. Sort of.

The companys new website, titled Apple Machine Learning Journal, is a bit grander than a blog. But it looks like it will have the same basic function: keeping readers up to date in a relatively accessible manner. Here, you can read posts written by Apple engineers about their work using machine learning technologies, says the opening post, before inviting feedback from researchers, students, and developers.

As the perennial question for bloggers goes, however: whats the point? What are you trying to achieve? The answer is familiar: Apple wants more attention.

Its clear that the recent focus on AI in the world of tech hasnt been kind to the iPhone maker. The company is perceived as lagging behind competitors like Google and Facebook, both in terms of attracting talent and shipping products. Other tech companies regularly publish new and exciting research, which makes headlines and gets researchers excited to work for them. Starting a blog doesnt do much to counter the tide of new work coming out of somewhere like DeepMind, but it is another small step into public life. Notably, at the bottom of Apples new blog, prominently displayed, is a link to the companys jobs site, encouraging readers to apply now.

Whats most interesting, though, is the blogs actual content. The first post (actually a re-post of the paper the company published last December, but with simpler language) deals with one of the core weaknesses of Apples AI approach: its lack of data.

Much of contemporary AIs prowess stems from its ability to sieve patterns out of huge stacks of digital information. Companies like Google, Amazon, and Facebook have access to a lot of user data, but Apple, with its philosophy of not snooping on customers in favor of charging megabucks for hardware has rather tied its hands in that regard. The first post on its machine learning blog offers a small riposte, describing a method of creating synthetic images that can be used to train facial recognition systems. Its not ground-breaking, but its oddly symbolic of what needs to be Apples approach to AI. Probably a blog worth following then.

Here is the original post:

Apple has started blogging to draw attention to its AI work - The Verge

Theres a lot of debate about what Artificial Intelligence really means, and how we should feel about it. Will it transform our world for the better? Will the machines take over? Will it simply make processes we already perform faster and smoother? As Gartner says in A Framework for Applying AI in the Enterprise, The artificial intelligence acronym AI might more appropriately stand for amazing innovations that do what we thought technology couldnt do.

One way and another, were talking about smart machines machines that are trained on existing, historical data, and use that to make accurate deductions or predictions about examples with which theyre presented. The applications are wide-ranging, from medicine to retail to self-driving cars and beyond.

For e-commerce, AI means the ability to deliver capabilities that simply were not possible before. There are two main directions in which this expresses itself:

1) Uncovering trends and audiences: A well-trained e-commerce AI can identify trends of buyer behavior, or interests in new products or experiences and adapt quickly.

2) Personalization: The experience can be tailored to each customer in ways that were not an option when companies had to configure/design the experience for everyone at once (or maybe have a few versions based on geographies). Customers can be offered the information and products they want, when they want them, in the ways that are best suited to them.

Why Ive Come to Love AI

As someone who travels a lot, I often have a fairly complex customer story when I shop online. I might be on a work trip to China, using a proxy to shop on a favorite U.S. store with my credit card, which has a New York billing address, sending something to an office in San Francisco to pick up on my next stop. Theres a good chance Ill be on a mobile device, and since I like to explore new things, Im often buying something of a kind Ive never bought before.

All of this makes me unpopular with e-commerce fraud prevention systems. Ive lost count of the number of times Ive been rejected, or delayed for days while my order is painstakingly reviewed. Sometimes Ive moved on by the time the package finally arrives at the place to which I had ordered it.

The thing is, I get it. I was a fraud prevention analyst myself, back in the time before AI was an option. I know exactly how hard these transactions are to get right, from the human perspective. I know how long it can take to review a transaction, and that as an analyst the tendency is always to play it safe even if that means sending a good customer away.

AI isnt a magic tool, but properly leveraging AI can enable retailers to eat the cake driving their sales upward by creating frictionless, speedy buying experiences for consumers and have it, too be completely protected against online payment fraud.

The 3 Unmatched Advantages of AI-based Fraud Protection Systems

Scale:An AI system can look at 6,000 data points in every transaction, and match them with billions of other transactions to look for patterns, discrepancies, and simple coincidences of events in just a fraction of a second. This means that all fraud decisions can happen 100 percent in real-time, regardless of how much traffic the site is receiving, or whether the fraud team is down with the flu.

Accuracy:In the last year a well-built and trained fraud protection AI has proven repeatedly that it outperforms even the best human reviewers in accuracy. For retailers the reduction in false declines (good customers mistakenly rejected as fraud) means more sales, and happier consumers, and the reduction in fraud chargebacks means lower costs, and lower risk. Beyond that, it enables new business models that were previously considered too risky, like the growing popularity of the try-and-buy model.

Adaptivity:In fraud prevention, one of the great challenges is the speed of learning necessary in order to deal with new fraudulent modaoperandi. If a fraudster finds a new technique that works, it will spread like wildfire and hundreds of fraudsters will attack thousands of retailers at once. An AI-based solution is the only realistic way for retailers to fight fraud together in this highly dynamic environment, combining their efforts and sharing data in a centralized way to prevent fraudsters from abusing one retailer after another. In fact, AI has the potential to actually reverse the asymmetry and push the fraudsters back. From the criminal point of view, if a new method to defraud is blocked almost immediately after it is first conceived and tried out, it isnt worth investing in.

AI is the future of e-commerce fraud prevention. It brings scale, accuracy and adaptivity to improve customer experience, block fraud and increase sales. Some retailers have already started leveraging AI, and theyre gaining a competitive advantage in this highly competitive field. Better fraud prevention is about to become standard. No site can afford to get left behind.

Michael Reitblat is chief executive officer of Forter.

For More Business News From WWD, See:

Amazon, Wal-Mart and Apple Top List of Biggest E-commerce Retailers

Consumer Preferences Reshaping Retail Landscape

Supima Design Competition Set for Sept. 7 at Pier 59 Studios

Excerpt from:

Think Tank: Is AI the Future of E-commerce Fraud Prevention? - WWD

Next in the list is SmartCast+, which is supposed to boast a lot more capabilities than the speakers with voice assistants of today, such as Amazon's Echo and its own Echo clone. If the smart speaker-projector ever becomes a real product, Lenovo wants to give it the ability to recognize sounds and objects, as well as to deliver AR experiences by projecting images onto a wall or a screen.

Lenovo is also eyeing the creation of an AI assistant called CAVA that's smarter than Siri and Alexa. The company wants to use deep learning to create facial recognition systems and natural language understanding technologies for the AI. That way, CAVA can truly understand your messages and make recommendations based on what you tell it. If you tell CAVA that you have a meeting in two hours, for instance, it can automatically check the weather and traffic conditions to tell you when to leave.

One of the last two concepts Lenovo showed off is the SmartVest, an ECG-equipped piece of clothing that can monitor your heart rhythm 24/7. The other one is the Xiaole customer service platform that can learn from interactions with customers in order to make each conversation more natural and personalized.

Lenovo says it sees cooking up concepts as an important part of its R&D process, because it lets the company explore and push boundaries. Unfortunately, there's no guarantee that any of them will become real products. We'd sure love to take those headsets and speaker-projector for a spin, though -- we'll just have to keep an eye on Lenovo's future releases.

View original post here:

Lenovo reveals AR headset and other ambitious AI concepts - Engadget

If you thought the rampant spread of text-based fake news was as bad as it could get, think again. Generating fake news videos that are undistinguishable from real ones is growing easier by the day.

A team of computer scientists at the University of Washington have used artificial intelligence to render visually convincing videos of Barack Obama saying things hes said before, but in a totally new context.

In a paper published this month, the researchers explained their methodology: Using a neural network trained on 17 hours of footage of the former US presidents weekly addresses, they were able to generate mouth shapes from arbitrary audio clips of Obamas voice. The shapes were then textured to photorealistic quality and overlaid onto Obamas face in a different target video. Finally, the researchers retimed the target video to move Obamas body naturally to the rhythm of the new audio track.

This isnt the first study to demonstrate the modification of a talking head in a video. As Quartzs Dave Gershgorn previously reported, in June of last year, Stanford researchers published a similar methodology for altering a persons pre-recorded facial expressions in real-time to mimic the expressions of another person making faces into a webcam. The new study, however, adds the ability to synthesize video directly from audio, effectively generating a higher dimension from a lower one.

In their paper, the researchers pointed to several practical applications of being able to generate high quality video from audio, including helping hearing-impaired people lip-read audio during a phone call or creating realistic digital characters in the film and gaming industries. But the more disturbing consequence of such a technology is its potential to proliferate video-based fake news. Though the researchers used only real audio for the study, they were able to skip and reorder Obamas sentences seamlessly and even use audio from an Obama impersonator to achieve near-perfect results. The rapid advancement of voice-synthesis software also provides easy, off-the-shelf solutions for compelling, falsified audio.

There is some good news. Right now, the effectiveness of this video synthesis technique is limited by the amount and quality of footage available for a given person. Currently, the paper noted, the AI algorithms require at least several hours of footage and cannot handle certain edge cases, like facial profiles. The researchers chose Obama as their first case study because his weekly addresses provide an abundance of publicly available high-definition footage of him looking directly at the camera and adopting a consistent tone of voice. Synthesizing videos of other public figures that dont fulfill those conditions would be more challenging and require further technological advancement. This buys time for technologies that detect fake video to develop in parallel. As The Economist reported earlier this month, one solution could be to demand that recordings come with their metadata, which show when, where and how they were captured. Knowing such things makes it possible to eliminate a photograph as a fake on the basis, for example, of a mismatch with known local conditions at the time.

But as the doors for new forms of fake media continue to fling open, it will ultimately be left to consumers to tread carefully.

Original post:

Researchers have figured out how to fake news video with AI - Quartz - Quartz

Clara Labs, creator of the Clara AI assistant, is announcing a $7 million Series A this morning led byBasis Set Ventures. Slack Fund also joined in the round, alongside existing investors Sequoia and First Round. The startup will be looking to further differentiate within the crowded field of email-centric personal assistants by building in features and integrations to address the needs of enterprise teams.

Founded in 2014, Clara Labs has spent much of the last three years trying to fix email. When CC-ed on emails, the Clara assistant can automatically schedule meetings reasoning around preferences like location and time.

If this sounds familiar, its because youve probably come across x.ai or Fin. But while all three startups look similar on paper, each has its own distinct ideology. Where Clara is running toward the needs of teams, Fin embraces the personal pains of travel planning and shopping. Meanwhile,x.ai opts for maximum automation and lower pricing.

That last point around automation needs some extra context. Clara Labs prides itself in its implementation of a learning strategy called human-in-the-loop. For machines to analyze emails, they have to make a lot of decisions is that date when you want to grab coffee, or is it the start of your vacation when youll be unable to meet?

In the open world of natural language, incremental machine learning advances only get you so far. So instead, companies like Clara convert uncertainty into simple questions that can be sent to humans on demand (think proprietary version of Amazon Mechanical Turk). The approach has become a tech trope with the rise of all things AI, but Maran Nelson, CEO of Clara Labs, is adamant that theres still a meaningful way to implement agile AI.

The trick is ensuring that a feedback mechanism exists for these questions to serve as training materials for uncertain machine learning models. Three years later, Clara Labs is confident that its approach is working.

Bankrolling the human in human-in-the-loop does cost everyone more, but people are willing to pay for performance. After all, even a nosebleed-inducing $399 per month top-tier plan costs a fraction of a real human assistant.

Anyone who has ever experimented with adding new email tools into old workflows understands that Gmail and Outlook have tapped into the dark masochistic part of our brain that remains addicted to inefficiency. Its tough to switch and the default of trying tools like Clara is often a slow return to the broken way of doing things. Nelson says shes keeping a keen eye on user engagement and numbers are healthy for now theres undoubtedly a connection between accuracy and engagement.

As Clara positions its services around the enterprise, it will need to take into account professional sales and recruiting workflows. Integrations with core systems like Slack, CRMs and job applicant tracking systems will help Clara keep engagement numbers high while feeding machine learning models new edge cases to improve the quality of the entire product.

Scheduling is different if youre a sales person and your sales team is measured by the total number of meetings scheduled, Nelson told me in an interview.

Nelson is planning to make new hires in marketing and sales to push the Clara team beyond its current R&D comfort zone. Meanwhile the technical team will continue to add new features and integrations, like conference room booking, that increase the value-add of the Clara assistant.

Xuezhao Lan of Basis Set Ventures will be joining the Clara Labs board of directors as the company moves into its next phase of growth. Lan will bring both knowledge of machine learning and strategy to the board. Todays Clara deal is one of the first public deals to involve the recently formed $136 million AI-focused Basis Set fund.

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Clara Labs nabs $7M Series A as it positions its AI assistant to meet ... - TechCrunch