Category Archives: Ai

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.

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

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.

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

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.

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Lenovo reveals AR headset and other ambitious AI concepts - Engadget

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

Hilarie Koplow-McAdams, former president of New Relic.Stuart Isett for Fortune Brainstorm Tech

Although artificial intelligenceor AIhas taken center stage in nearly all technology discussions, it's unclear that every business needs an "AI strategy."

Initially, most attendees of a Fortune Brainstorm Tech conference panel in Aspen, Colo. on Tuesday agreed with the assertion that all companies need an AI game plan. But that consensus withered after just a little while.

For small companies, in particular, it probably makes no sense to dedicate limited resources to hire an AI expert, even if there were one available. It was also unclear how a mom-and-pop business, say a tailor shop, could benefit from AI, although Zachary Bogue, co-managing partner of the Data Collective, an early-stage tech venture fund, said that robots can already handle and sew fabric.

"Maybe every industry needs an AI strategy, but not every business," said George Kurtz, CEO of CrowdStrike, a cybersecurity specialist.

Related: Beware the Hype of AI

"AI is just beginning, so having a strategy around it is a problem because you have to define what you're talking about," said Norman Winarski, founder of Winarski Ventures. "A lot of industries will not do well by deploying chatbots," he added, referring to those automated customer service agents that are increasingly used by companies online. "You have to be incredibly careful in how you deploy an AI solution, you need to think about how people will react, and it takes a lot of resources."

And small businesses, as noted, have limited resources. And, given that small companies are the vast majority of American businesses, this is a rather large gap in the AI picture.

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Having said that, it's likely that many small businesses will end up using software that incorporates AI, said Hilary Koplow-McAdams, former president of New Relic ( newr ) and now board member of Tableau ( data ) . Virtually every business software company from Salesforce ( crm ) to Microsoft ( msft ) says they are weaving AI capabilities into their products.

Several attendees were skeptical of the notion that all applications are under the AI umbrella is wrong. Expert systems technology that emulates human decision processes doesn't necessarily require a lot of data, for example, said Michael Schrage, fellow for MIT's Center for Digital Business.

While machine learning, by its nature, requires large amounts of data in order to learn to recognize patterns, there are other jobs in which simple rules or operations can be automated without a lot of data.

"If you compete just on massive data, youve already lost to Facebook ( fb ) , Google ( goog ) , and Microsoft ( msft ) you've already lost," he said.

And, in those cases where a company does have a lot of data at its disposal, that data won't isn't valuable for AI if it is not formatted and prepared correctly. Several attendees stressed the need for "data refineries" to clean that data.

"If your data is not cleansed, you cannot use AI," Kurtz said.

So in this new-age era of AI, the old maxim still applies: Garbage in, garbage out.

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Do You Need an Artificial Intelligence Strategy? The Answer May Surprise You - Fortune

Syte.ai, a visual search startup focused on fashion products, announced that it has raised an $8 million Series A. The lead investor is NHN Ventures (the investment arm of Korean internet services giant NHN), with participation from Naver Corp., messaging app Line Corp., Magma VC, Remagine VC, KDC Ventures, and NBM Ventures.

Co-founder and CMO Lihi Pinto Fryman was working as an investment banker in London when a red dress in Vogue caught her eye. She tried to find a similar one online, but couldnt.

I said to my husband, how can it be that in 2014 I see a dress that I really like and cant just tap it and get it? she told TechCrunch. The two started looking at ways to build a better visual search engine and in 2015 teamed up with CTO Dr. Helge Voss and COO Idan Pinto to launch Syte.ai (Lihis husband, Ofer Fryman, a former account executive at Hewlett-Packard, is CEO).

Syte.ais Series A will be used for marketing and growth in the U.S., where it hopes to sign up large fashion publishers and retailers. Of course, with Syte.ais new roster of investors, its fair to assume that it will also look for deals in Asia. Fryman declined to talk about potential partnerships, but said Syte.ais new Asian backers, including NHN and Line, have been searching for a while to find the most accurate deep-learning technology that can make images shoppable.

In a prepared statement, Woo Kim, managing director and partner of NHN Investment, said that many next-generation search solutions have delivered disappointing results so far.

It was largely because such below-average image search results were driven by essentially the same deep learning approach, and the only differentiation was how many sample images you have to train your database, he added.Syte.ais unique approach of redefining how machines understand images is simply ground-breaking innovation. We believe thatSyte.ai will disrupt the way industry adopts image search technology.

Syte.ais founders have spent the last three years developing its deep-learning algorithms, which Fryman describes as building a bridge between physics and fashion. Fryman says Syte.ai is different from its competitors because its deep learning-based search engine focuses only on fashion products, even though there are other verticals, such as home decor, where visual search is also in demand. Its main business are search tools for online publishers and retailers, but it also has several consumer products, including a Chrome extension called Fashion Lover, and Glamix, a chatbot.

The startup is just one of several that are tackling visual search as online businesses try to reduce their dependency on banner ads and find ways to monetize that are better suited to mobile screens. Other companies in the same space include Slyce, Clarifai, and Visenze, which is itself funded by another one of Asias leading Internet firms, Rakuten.

The reason we chose fashion is because its so hard to recognize, says Fryman. Think about your top. It looks completely different if you are wearing it, or if its on a hanger, or in a catalog, or if you are walking on a red carpet or sitting down. Its hard to teach the machine its the same item.

After a site integrates Syte.ais search technology, users can hover their cursor over an item in a photo and automatically get results for similar products that are on sale. For publishers, including fashion blogs and magazines, Syte.ai displays items from a range of stores and price points to increase the chances that the user will click on at least one result. The company monetizes by sharing revenue with publishers and charging e-commerce stores a subscription fee.

One of the main attractions for online stores is that Syte.ais search engine can help customers find alternative items if what they want is out of stock. According to a study by IHL Group, out-of-stocks cost retailers about $634.1 billion a year. It also helps sites turn over indexed inventory, or items overlooked by customers because they arent on the front page (at larger sellers, this can potentially be hundreds of items). Visual search is especially crucial for mobile shopping, where customers want to see as many results displayed as quickly as possible on their small screens. Once Syte.ai perfects visual search for fashion, Fryman says, it will move onto other verticals.

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Syte.ai, a visual search startup just for fashion, closes $8M Series A ... - TechCrunch

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Elon Musk's Freak-Out Over Killer Robots Distracts from Our Real AI Problems - WIRED

Elon Musk has branded artificial intelligencea fundamental existential risk for human civilisation.

He says we mustnt wait for a disaster to happen before deciding to regulate it, and that AI is, in his eyes, the scariest problem we now face.

He also wants the companies working on AI to slow down to ensure they dont unintentionally build something unsafe.

The CEO of Tesla and SpaceX was speaking on-stage at the National Governors Association at the weekend.

I have exposure to the most cutting-edge AI and I think people should be really concerned about it, he said. I keep sounding the alarm bell but until people see robots going down the street killing people, they dont know how to react because it seems so ethereal.

I think we should be really concerned about AI and I think we should AIs a rare case where I think we need to be proactive in regulation instead of reactive. Because I think by the time we are reactive in AI regulation, its too late.

Normally the way regulations are set up is that a whole bunch of bad things happen, theres a public outcry, and then after many years, a regulatory agency is set up to regulate that industry. Theres a bunch of opposition from companies who dont like being told what to do by regulators. It takes forever.

That, in the past, has been bad but not something which represented a fundamental risk to the existence of civilisation. AI is a fundamental risk to the risk of human civilisation, in a way that car accidents, airplane crashes, faulty drugs or bad food were not. They were harmful to a set of individual in society, but they were not harmful to society as a whole.

AI is a fundamental existential risk for human civilisation, and I dont think people fully appreciate that.

However, he recognises that this will be easier said than done, since companies dont like being regulated.

Also, any organisation working on AI will be crushed by competing companies if they dont work as quickly as possible, he said. It would be up to a regulator to control all of them.

When its cool and regulators are convinced that its safe to proceed, then you can go. But otherwise, slow down.

He added: I think wed better get on [introducing regulation] with AI, pronto. Therell certainly be a lot of job disruption because whats going to happen is robots will be able to do everything better than us. Im including all of us.

Earlier this year, Mr Musk said that humans will have to merge with machines to avoid becoming irrelevant.

Ray Kurzweil, a futurist and Googles director of engineering, believes that computers will have human-level intelligence by 2029.

However, he believes machines will improve humans, making us funnier, smarter and even sexier.

Here is the original post:

Elon Musk: AI is a 'fundamental existential risk for human civilisation' and creators must slow down - The Independent

Many enterprise software vendors are focused on the goal of simply building and marketing an AI-based product rather than identifying use cases and the business value to customers.

So says Gartner analyst Jim Hare in a July 6th piece of research titled How Enterprise Software Providers Should (and Should Not) Exploit the AI Disruption.

Nearly every technology provider is now claiming to be an AI company, Hare writes, having counted more than 1,000 vendors who claim to either sell AI or bake it into their products. This ultrahype of the AI label has led to a hysteria of 'rebranding' from companies desperate to keep up. Similar to the go-go days of the late 1990s, when every enterprise was an 'ebusiness' company, many vendors are entering the AI market by simply adding 'AI' to their sales and marketing materials.

Similar to greenwashing, in which companies exaggerate the environmental-friendliness of their operational practices for business benet, many technology vendors are now 'AI washing' by applying the AI label a little too indiscriminately.

That's not helpful, he argues, because AI-washing often contains nothing more than empty promises.

Some vendors are promoting AI brands as if they are superheroes (such as Einstein, Holmes, Leonardo and Watson) that can save the world, he says. While it is creative brand marketing, it misdirects buyers and increases confusion as to what is real and what is just marketing.

He also says plenty of AI isn't, as follows:

Ouch. And double ouch for VendorLand because Hare says users are already seeing through the hype and just not buying AI while they wait for the hype to die down. He also warns that those who do buy AI now are at risk of becoming disillusioned by products that over-promise, under-deliver and leave buyers wary of buying any more AI any time soon.

Hare therefore urges marketers to tone it down, drop the term AI from their web pages and hop off the hype-train if they want to be differentiated or have a serious discussion about how their wares use machine learning.

He remains a believer in AI, however, as his document predicts it will be pervasive by 2020 and that it can work well when used to improve the performance of existing systems, rather than as a big-bang upgrade.

But he also warns that even when taking that path of least resistance, organisations will struggle to adopt AI because few have the skilled people to sift through the masses of supposedly artificially-intelligence products on offer, never mind keep them running once installed.

Here is the original post:

Vendors rush to call everything AI even if it isn't, or doesn't help - The Register

Artificial intelligence is poised to continue advancing until it is everywhere and before it gets there, Tolga Kurtoglu wants to make sure its trustworthy.

Kurtoglu is the CEO of Parc, the iconic Silicon Valley research and development firm previously known as Xerox Parc. Although its best known for its pioneering work in the early days of computing developing technologies such as the mouse, object-oriented programming and the graphical user interface Parc continues to help companies and government agencies envision the future of work.

A really interesting project that were working on is about how to bring together these AI agents, or computational agents, and humans together, in a way that they form sort of collaborative teams, to go after tasks, Kurtoglu said on the latest episode of Recode Decode, hosted by Kara Swisher. And robotics is a great domain for exploring some of the ideas there.

Whereas today you might be comfortable asking Apples Siri for the weather or telling Amazon's Alexa to add an item to your to-do list, Kurtoglu envisions a future where interacting with a virtual agent is a two-way street. You might still give it commands and questions, but it would also talk back to you in a truly conversational way.

What were talking about here is more of a symbiotic team between an AI agent and a human, he said. They solve the problems together, its not that one of them tells the other what to do; they go back and forth. They can formulate the problem, they can build on each others ideas. Its really important because were seeing significant advancements and penetration of AI technologies in almost all industries."

You can listen to Recode Decode on Apple Podcasts, Google Play Music, Spotify, TuneIn, Stitcher and SoundCloud.

Kurtoglu believes that both in our personal lives and in the office, every individual will be surrounded by virtual helpers that can process data and make recommendations. But before artificial intelligence reaches that level of omnipresence, it will need to get a lot better at explaining itself.

"At some point, there is going to be a huge issue with people really taking the answers that the computers are suggesting to them without questioning them, he said. So this notion of trust between the AI agents and humans is at the heart of the technology were working on. Were trying to build trustable AI systems.

So, imagine an AI system that explains itself, he added. If youre using an AI to do medical diagnostics and it comes up with a seemingly unintuitive answer, then the doctor might want to know, Why? Why did you come up with that answer as opposed to something else? And today, these systems are pretty much black boxes: You put in the input, it just spits out what the answer is.

So, rather than just spitting out an answer, Kurtoglu says virtual agents will explain what assumptions they made and how they used those assumptions to reach a conclusion: Here are the paths Ive considered, here are the paths I've ruled out and heres why.

If you like this show, you should also sample our other podcasts:

If you like what were doing, please write a review on Apple Podcasts and if you dont, just tweet-strafe Kara.

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Humans and AI will work together in almost every job, Parc CEO Tolga Kurtoglu says - Recode