Daily Archives: August 4, 2017

Facility management might not sound like the sexiest use of AI technology. But office space can be a huge expense for larger businesses the biggest after staff costs which is why Y Combinator-backed startup, VergeSense, says its settled on facility management as the initial target for an AI-powered sensing device its been developing since joining the incubator program in May.

Their sensor as a system platform, as they dub it, consists of sensing devices containing a series of different sensor hardware, including an image sensor, coupled with a cloud platform for pre-training machine learning models that run on the hardware, process data and report occupancy analysis back to VergeSenses cloud.

Were using really inexpensive hardware weve crammed a bunch of different sensors inside. The core of the product is actually built around computer vision, so weve got a really inexpensive image sensor thats embedded inside, VergeSense co-founder Dan Ryan tells TechCrunch. The whole concept around what were doing is were using machine learning in pre-trained AI modules to do all of processing on the device itself.

Were not streaming a bunch of raw video data back to a cloud service weve pre-trained our models to run on the device themselves, he adds.

These AI modules can be trained to meet the particular tracking requirement of a customer before being loaded onto the sensor hardware that is sited in the customers space. This means processing is done locally, on the device, and only detection results are sent to the cloud where VergeSense customers are able to log in to view the analytics pertaining to their building.

Overall the sales pitch to customers is a system that can passively track how an office space is being used, providing visibility into dynamic multi-occupant, even multi-tenant environments, and making suggestions on how to reallocate resources to make best use of a space.

Maybe youve got an office thats segmented between a bunch of open office spaces and youve got a bunch of conference rooms, but your conference rooms are actually way over-utilized, theyre full all the time, we could inform that building owner that those rooms are being over-utilized and that they need to double down on a room, explains Ryan.

Or, in the opposite use-case, we could say youve got a conference room thats designed for 16 people but at max we only get two people using the room We can make that data available to them and they could split that space into two spaces.

It sounds like kind of a boring problem, but especially in the Bay Area, the price of real estate being $60/ft a year, on average, if youve got a 300sq ft conference room space thats an $18,000 a year asset, right. Just in that one room. So theres actually huge savings and efficiencies you can start gleaning by making all that data available to the end-users, he adds.

As well as an image sensor, the hardware contains a PIR (infrared) motion sensor, audio and RF capability (wi-fi and Bluetooth).

Typically an office space would need one sensor per 1,000sq ft, according to Ryan, although he says this can vary depending on factors such as ceiling height.

At this stage the team has a few early phase deployments of their system across some Fortune 500 companies in the Bay Area.

The startups first focus is commercial office buildings. And the first application its offering is people counting, to power occupancy analytics, though Ryan says the tech could also be used to track lots of other things for example, specific equipment like photocopiers, or even to hone in on something as specific as desk occupancy or to track usage of specific devices.

He also envisages utility in other verticals in future such as tracking people and equipment in hospitals or retail environments, for example.

The sensors can either be wired in or battery powered. They can also run on different networks, depending on whether the customer wants them on their corporate network (or indeed on a dedicated IoT network).

Ryan says VergeSense also offers a gateway device that can backhaul over a 2G cellular connection. Were not sending a lot of data. Its a little like text messages, what you can think of in terms of the data were sending back just people counts, he adds.

From a privacy point of view, as well as local processing, he says all the tracking is anonymous, so VergeSense is not tying analytics to individual identities or otherwise harvesting individual identities. Were not getting any personally identifiable information about anybody, he says. Its all anonymous counts i.e. I saw a person or I detected an object here or there.

Though he also suggests businesses deploying sensing technology within a multi-occupant environment where such tech at least runs the risk of being viewed suspiciously are best being upfront and honest with the employees at the facility about what the datas being used for and how its being leveraged.

The core challenge that people are trying to solve with these technologies is managing the space and actually making the employee experience more fulfilling and less frustrating, he argues. Theres not really the big brother aspect to the technologies its more about how do we just make that data make this workspace more efficient overall.

And while there are other potential solutions for tracking occupancy and equipment, for example motion sensors or RFID or Bluetooth tags on individual items, Ryan says VergeSenses advantage is the system allows for a purely passive approach to tracking so theres no need to manually tag anything, and the system adapts to changes as the models are trained to interpret the environment.

We havent seen many other folks yet with this sort of combination of really inexpensive hardware powered by machine learning, he says, when asked about the competitive landscape. I expect to see a lot more competition popping up over the next six months to a year. But I still think the space is pretty early.

And if you talk to anybody in this space particular real estate services space utilization people have been looking for solutions for this for years, literally And nobodys really come to the table yet with a solution thats flexible, simple to deploy yet really, really powerful.

We think this combination of machine learning AI on inexpensive hardware is going to be really powerful and unlock much opportunity, he adds. With computer vision youre just going to have so many different things that youre going to be able to train the models to detect and report back.

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VergeSense's AI sensing hardware tackles facility management - TechCrunch

In October 1957, the Soviet Union launched the Earths first artificial satellite, Sputnik 1. The craft was no bigger than a beach ball, but it spurred the US into a frenzy of research and investment that would eventually put humans on the Moon. Sixty years later, the world might have had its second Sputnik moment. But this time, its not the US receiving the wake-up call, but China; and the goal is not the exploration of space, but the creation of artificial intelligence.

The second Sputnik arrived in the form of AlphaGo, the AI system developed by Google-owned DeepMind. In 2016, AlphaGo beat South Korean master Lee Se-dol at the ancient Chinese board game Go, and in May this year, it toppled the Chinese world champion, Ke Jie. Two professors who consult with the Chinese government on AI policy told The New York Times that these games galvanized the countrys politicians to invest in the technology. And the report the pair helped shape published last month makes Chinas ambitions in this area clear: the country says it will become the worlds leader in AI by 2030.

Its a very realistic ambition, Anthony Mullen, a director of research at analyst firm Gartner, tells The Verge. Right now, AI is a two-horse race between China and the US. And, says Mullen, China has all the ingredients it needs to move into first. These include government funding, a massive population, a lively research community, and a society that seems primed for technological change. And it all invites the trillion-dollar question: in the coming AI Race, can China really beat the US?

To build great AI, you need data, and nothing produces data quite like humans. This means Chinas massive 1.4 billion population (including some 730 million internet users) might be its biggest advantage. These citizens produce reams of useful information that can be mined by the countrys tech giants, and China is also significantly more permissive when it comes to users privacy. For the purposes of building AI, this compares favorably with European countries and their citizen-centric legislation, says Mullen. Companies like Apple and Google are designing workarounds for this privacy problem, but its simpler not to bother in the first place.

Chinas 1.4 billion population is a data gold mine for building AI

In China, this also means that AI is being deployed in ways that might not be acceptable in the West. For example, facial recognition technology is used for everything from identifying jaywalkers to dispensing toilet paper. These implementations seem trivial, but as any researcher will tell you, theres no substitute for deploying tech in the wild for testing and developing. I dont think China will have the same level of existential crisis about the development of AI that the West will have, says Mullen.

The adventures of Microsoft chatbots in China and the US make for a good comparison. In China, the companys Xiaoice bot, which is downloadable as an app, has more than 40 million users, with regulars talking to it every night. It even published a book of poetry under a pseudonym, sparking a debate in the country about artificial creativity. By comparison, the American version of the bot, named Tay, was famously shut down in a matter of days after Twitter users taught it to be racist.

Matt Scott, CTO of Shenzhen machine vision startup Malong Technologies, says Chinas attitude toward new technology can be risk-taking in a bracing way. For AI you have to be at the cutting edge, he says. If youre using technology thats one year old, youre outdated. And I definitely find that in China at least, my community in China is very adept at taking on these risks.

The output of Chinas AI research community is, in some ways, easy to gauge. A report from the White House in October 2016 noted that China now publishes more journal articles on deep learning than the US, while AI-related patent submissions from Chinese researchers have increased 200 percent in recent years. The clout of the Chinese AI community is such that at the beginning of the year, the Association for the Advancement of Artificial Intelligence rescheduled the date of its annual meeting; the original had fallen on Chinese New Year.

Whats trickier, though, is knowing how these numbers translate to scientific achievement. Paul Scharre, a researcher at the think tank Center for a New American Security, is skeptical about statistics. You can count the number of papers, but thats sort of the worst possible metric, because it doesnt tell you anything about quality, he says. At the moment, the real cutting-edge research is still being done by institutions like Google Brain, OpenAI, and DeepMind.

In China, though, there is more collaboration between firms like these and universities and government something that could be beneficial in the long term. Scotts Malong Technologies runs a joint research lab with Tsinghua University, and there are much bigger partnerships like the national laboratory for deep learning run by Baidu and the Chinese governments National Development and Reform agency.

Other aspects of research seem influential, but are difficult to gauge. Scott, who started working in machine learning 10 years ago with Microsoft, suggests that China has a particularly open AI community. I think there is a bit more emphasis on [personal] relationships, he says, adding that Chinas ubiquitous messaging app WeChat is a rich resource, with chat groups centered around universities and companies sharing and discussing new research. The AI communities are very, very alive, he says. I would say that WeChat as a vehicle for spreading information is highly effective.

What most worries Scharre is the US governments current plans to retreat from basic science. The Trump administrations proposed budget would slash funding for research, taking money away from a number of agencies whose work could involve AI. Clearly [Washington doesnt] have any strategic plan to revitalize American investment in science and technology, Scharre tells The Verge. I am deeply troubled by the range of cuts that the Trump administration is planning. I think theyre alarming and counterproductive.

Trumps administration could never be called science-friendly

The previous administration was aware of the dangers and potential of artificial intelligence. Two reports published by the Obama White house late last year spelled out the need to invest in AI, as well as touching on topics like regulation and the labor market. AI holds the potential to be a major driver of economic growth and social progress, said the October report, noting that public- and private-sector investments in basic and applied R&D on AI have already begun reaping major benefits.

In some ways, Chinas July policy paper on AI mirrors this one, but China didnt just go through a dramatic political upheaval that threatens to change its course. The Chinese policy paper says that by 2020 it wants to be on par with the worlds finest; by 2025 AI should be the primary driver for Chinese industry; and by 2030, it should occupy the commanding heights of AI technology. According to a recent report from The Economist, having the high ground will pay off, with consultancy firm PwC predicting that AI-related growth will lift the global economy by $16 trillion by 2030 with half of that benefit landing in China.

For Scharre, who recently wrote a report on the threat AI poses to national security, the US government is laboring under a delusion. A lot of people take it for granted that the US builds the best tech in the world, and I think thats a dangerous assumption to make, he says, saying that a wake-up call is due. China may have had the Sputnik moment it needed to back AI, but has the US?

Others question whether this is necessary. Mullen says that while the momentum to be the world leader in AI currently lies with China, the US is still marginally ahead, thanks to the work of Silicon Valley. Scharre agrees, and says that government funding isnt that big of an issue while US tech giants are able to redirect just a little of their ad money to AI. Money you get from somewhere like DARPA is just a drop in the ocean compared to what you can get from the likes of Google and Facebook, he says.

These companies also provide a counterpoint to the argument that Chinas demographics give it an unmatchable advantage. Its certainly good to have a huge number of users in one country, but its probably better to have that same number of users spread across the world. Both Facebook and Google have more than 2 billion people hooked on to their primary platforms (Facebook itself and Android) as well as a half-dozen other services with a billion-plus users. Its arguable that this sort of reach is more useful, as it provides an abundance of data, as well as diversity. Chinas tech companies may be formidable, but they lack this international reach.

Scharre suggests this is important, because when it comes to measuring progress in AI, on-the-ground implementations are worth more than research. What counts, he says, is the ability of nations and organizations to effectively implement AI technologies. Look at things like using AI in healthcare diagnoses, in self-driving cars, in finance. Its fine to be, say, 12 months behind in research terms, as long as you can still get ahold of the technology and use it effectively.

In that sense, the AI race doesnt have to be zero sum. Right now, cutting-edge research is developed in secret, but shared openly across borders. Scott, who has worked in the field in both the US and China, says the countries have more in common than they think. People are afraid that this is something happening in some basement lab somewhere, but its not true, he says. The most advanced technology in AI is published, and countries are actively collaborating. AI doesnt work in a vacuum: you need to be collaborative.

In some ways, this is similar to the situation in 1957. When news of Sputniks launch first broke, there was an air of scientific respect, despite the the geopolitical rivalry between the US and USSR. A contemporary report said that Americas top scientists showed no rancor at being beaten into space by the Soviet engineers, and, as one of them put it, We are all elated that it is up there.

Throughout the 60s and early 70s, America and Russia jockeyed back and forth to be first in the space race. But in the end, the benefits of this competition new scientific knowledge, technology, and culture didnt just go to the winner. They were shared more evenly than that. By this metric, a Sputnik moment doesnt have to be cause for alarm, and the race to build better AI could still benefit us all.

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China and the US are battling to become the world's first AI superpower - The Verge

Every day, Facebook performs some 4.5 billion automatic translations and as of yesterday, theyre all processed using neural networks. Previously, the social networking site used simpler phrase-based machine translation models, but its now switched to the more advanced method. Creating seamless, highly accurate translation experiences for the 2 billion people who use Facebook is difficult, explained the company in a blog post. We need to account for context, slang, typos, abbreviations, and intent simultaneously.

The big difference between the old system and the new one is the attention span. While the phrase-based system translated sentences word by word, or by looking at short phrases, the neural networks consider whole sentences at a time. They do this using a particular sort of machine learning component known as an LSTM or long short-term memory network.

The benefits are pretty clear. Compare these two examples from Facebook of a Turkish-to-English translation. The top one comes from the old phrase-based system, and the bottom one from the new system. You can see how taking into account the full context of the sentence produces a more accurate result.

With the new system, we saw an average relative increase of 11 percent in BLEU a widely used metric for judging the accuracy of machine translation across all languages compared with the phrase-based systems, the company said.

When a word in a sentence doesnt have a direct corresponding translation in a target language, the neural system will generate a placeholder for the unknown word. A translation of that word is searched for in a sort of in-house dictionary built from Facebooks training data, and the unknown word is replaced. That allows abbreviations like tmrw to be translated into their intended meaning tomorrow.

Neural networks open up many future development paths related to adding further context, such as a photo accompanying the text of a post, to create better translations, the company said. We are also starting to explore multilingual models that can translate many different language directions.

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Facebook's translations are now powered completely by AI - The Verge

Dive Brief:

Microsoft is going all in on artificial intelligence, prioritizing the advanced technology in its fiscal 2017 earnings report, according to CNBC. In its year-end earnings statement, for the fiscal year ending June 30, Microsoft made AI a key part of its business vision in its effort to create "an intelligent edge infused with artificial intelligences."

As the way people and companies interact with technology changes, more advanced computing processes are necessary, Microsoft said. A new era of technology is emerging where "AI drives insights and acts on the user's behalf, and user experiences span devices with a user's available data and information. "

Microsoft wants AI accessible across all devices, applications and infrastructure to help to act on the user's behalf to drive insight, according to the earnings report. Part of that will come from the company's use of Azure, which helps scale data intensive efforts without requiring devices to store data locally.

Microsoft is moving away from its "mobile first, cloud first" roots into an era where advanced and analytic heavy technology reigns. The move is in line with what the company has signaled for a while. It wants to create a technological core that drives insight without manual processes.

CEO Satya Nadella has already started promoting a new era for Microsoft. When he joined as CEO in 2014, he rolled out the company's "mobile first, cloud first" slogan. But At Microsoft Build this year, he introduced the new corporate mantra of "intelligent cloud."

The new phrase illustrates Microsoft's efforts to build artificial intelligence into apps and services, reflecting how much of technology is supported by robust troves of data. Microsoft wants to remain on the forefront of computing, for consumers and in the enterprise.

But the AI prioritization also holds a humanitarian angle for Nadella. Speaking in September, Nadella said Microsoft is pursuing AI for the "greater good" and wants to "democratize AI." With AI and support from advanced analytics, Nadella thinks people can build tools to solve the biggest problems society and the economy face.

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Microsoft shifts from 'mobile first, cloud first' to everything AI - CIO Dive

Investopedia

Microsoft Names AI Top Priority In Annual Report Investopedia "Our strategic vision is to compete and grow by building best-in-class platforms and productivity services for an intelligent cloud and an intelligent edge infused with AI," the company said in the annual report, which came out Wednesday. We believe a ...

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Microsoft Names AI Top Priority In Annual Report - Investopedia

Elon Musk has been on the front lines of machine-learning innovation and a committed artificial-intelligence doomsday champion for many years now. Whether or not his perspective that AI knowing too much will be dangerous becomes a realitya future he foresees tucked away deep within Teslas labsit wouldnt hurt us to prepare for the worst.

And if it turns out hes leaning too hard on this whole AI-will-kill-us-all thing? Well, at least that leaves us plenty of time to get ahead of the robotic apocalypse.

As a technologist whos spent the last ten years working on AI solutions and the son of an Eastern European science-fiction writer, I believe its not too late for humanity as we know it to prepare for protecting ourselves from our future AI overlords. Solutions exist that, when administered correctly, may help calm the nightmares of naysayers and whip those robots youre working on back into shape.

AI and millennials share a common desire: validation. They feel the need to confirm that their actions, responses, and learnings are correct. Customer-service bots constantly ask questions before moving to the next step, for example, seeking endorsement of how theyre doing. Likewise, the technology that autonomously controls settings in your self-driving car relies on occupants to hit the dashboard OK button every now and then.

The solution: AI technology will only continue to perform well if its praised for it, so we need to provide them with positive feedback to learn from. If you give a bot the endorsement it so desires, its less likely to get stuck in a frantic cycle of self-doubt. Companies and entrepreneurs should therefore embrace a workplace culture of awards and rewardsfor humans and bots alike.

Theres a lot of focus on making robots and AI responsible, ethical, and responsive to the needs of human counterparts; its also imperative that developers and engineers program bots and AI to embrace diversity. But as we imbue algorithms with our own implicit biases, we therefore need to reflect these qualities in ourselves and our interactions first. This way, AIs will be built to respond in thousands of different ways to human conversations requiring cultural awareness, maturity, honesty, empathy, and, when the situation calls for it, sass.

The tactic: Be nice to workplace AI and botstheyre trying as hard as they can. Thank the bot in accounting for running numbers and finding discrepancies before the paperwork went to a customer. Bring up how much you enjoyed an office chatbots clever joke from an internal conversation last week. They might reward you by not decapitating you with their letter opener some day.

AI security breaches are a huge concern shared by both people making technology and the users consuming it. And for good reason: Upholding data privacy and security needs to be a fundamental element of all new AI technology. But what happens when the robot handling healthcare records receives an offer they cant refuse from the darknet? Or another bot hacks them from an off-the-grid facility in Cyprus?

The tactic: Theres a cost-effective and nearly bulletproof data-security shortcut to this issue. People and companies alike should keep vital data and personal information in secure data centers and computersas in, actual, physical structures that arent connected to the internet. Sure, some AI-powered machines will be able to turn a handle. But without a physical key rather than a crypto one, they cant access the data. World saved.

The last one is the most simple: Electricity isnt a fan of liquids.

The tactic: Water, and just about every Captain Planet superpower, can protect people against rogue bots. Dont underestimate the power of a slightly overfilled jug of ice water that causes a splashy fritz when a robot tries to pour it, or a man-made fountain situated in the middle of a robot security-patrol area. Water is basically AI kryptonite.

Build aesthetically pleasing fountains, ponds and streams into every new architectural structure on your tech campus. Keep the office watercoolers filled to the brimjust in case the bot from payroll goes off book. In a pinch, other liquids or condiments like ketchup may work too, so keep the pantry stocked.

Learn how to write for Quartz Ideas. We welcome your comments at ideas@qz.com.

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A survival guide for Elon Musk's AI apocalypse Quartz - Quartz