Category Archives: Ai

Artificial intelligence is all over the news, with tech titans arguing over whether it will be a force for good or bad. An equally important question is whether AI will stratify society even more, and create a world of haves and have nots.

AI is already impacting multiple industries and will take over many blue collar and white collar jobs in the years to come. The speed and severity with which this happens are what creates the biggest challenges for the US and countries around the world. Add to this the geopolitical implications, recently outlined in an important op ed by Kai Fu Lee, and even weak AI can be seen as a scary thing.

So, we need to be proactive and create alternative career paths as AI impacts jobs and takes away many employment opportunities. Lets look at what this means in the near term (next decade), medium term (10-20 years) and long-term (20-plus years).

As AI grows in the coming years, mostly blue collar jobs will first be impacted. The political reality is that this will likely not cause major policy changes as higher earners remain largely unaffected by job changes and possibly benefit from AIs positives. As autonomous vehicles run by AI take over from taxi drivers (and make transportation more reliable, faster and open up spaces currently occupied by parking garages) and robots with AI take over all but specialized work on factory floors (making production costs lower which hopefully translates into less expensive goods), blue collar workers will have few alternatives to pivot to in their careers.

We will likely see increased polarization in society unless programs are put into place early on to create soft landings through training in careers which cannot be automated easily. For some, this could be jobs with heavy interpersonal interactions, for others learning the basics of working with and programming AI. Overall though, it is likely to be a tough time for those without a strong education base.

By the late 2020s, AI will become commonplace and most blue collar jobs will likely be a shadow of their former selves. In addition, white collar workers in areas including healthcare and financial services will also be under pressure: who needs a lab technician to read your X-ray when an AI can do it faster, cheaper and at least as well? To be sure, white collar workers are going to be under pressure long before this, but it will take some time before the professional class sees their career options change markedly. For better or worse, the time that this does happen is when we are likely to see major societal changes.

More white collar workers will transition to jobs that can only be done by humans, but this, too, will be limited. Low-level programmers who understand coding may be able to quickly learn how to program an AI, but others outside of tech, like lawyers many of whose jobs will be eliminated will face a much more daunting transition to new careers.

There is no consensus, but within the next twenty years, we will likely see the emergence of AI at least as smart as human beings. This could lead to huge benefits for society by allowing a benevolent strong AI to work with and for human beings, a highly classist society where the haves who own the strong AI and have nots who do not live in conflict, or possibly a merging of human and AI such that we become something greater than we currently are.

For the lucky few who own and work for the companies that control the best AI, they may consolidate the wealth, power and insight to dominate society. This brings up a fundamental question of whether AI should be controlled by large tech companies or disseminated more broadly? To complicate things, AI works best by leveraging network effects, so breaking up the Amazons and Baidus of the world into smaller enterprises would be foolish and outmoded. Whoever owns and controls the best AI, the network effects need to be maintained otherwise the benefits of AI are destroyed. To be sure, Google and others have opened up some AI tools to the masses, but clearly, they have and will keep the best tech for themselves.

A number of our best minds believe that the rise and concentration of AI will require tax rates to be increased to fund social welfare for the large part of society that will be displaced. This is certainly one option that has merits (allowing people to perform useful but currently underpaid jobs or freeing them to become lifelong learners), but whether this is done through a Universal Basic Income or another form it may prove difficult to achieve this without open conflict.

Another option is to make the big data that will feed AI and basic AI modules available to all a creative commons of data and AI. This could enable blue collar and white collar professionals alike to innovate and create small and medium sized businesses that leverage the growth of AI. This would require strong government intervention but also empower the private sector rather than taxing it.

A further option is to place the best AI in the hands of the government itself and allow people to pursue their passions while having their basic needs attended to from the wealth generated by government. This is the Star Trek future of science fiction but is a distinct possibility if we get comfortable with everyone receiving a government hand out. Indeed, the concept of money itself would be outmoded in such a society.

Added to all of this are the foreign policy implications of AI, which Kai Fu Lee correctly addresses in his recent writing. So if you are living in the US or China consider yourself lucky: your government has far more choice (and say) when it comes to the rise of AI. At the very least, these two nations will not have to grapple with the limited power that arises from reacting to the technological revolutions of others.

So what does this all mean? Are we on a path to a world of haves and have nots? Maybe but we have several alternate paths we can take if we are honest, thoughtful, and forward-thinking. AI is here to stay and will create many positive outcomes. The negative depictions in science fiction may or may not happen. In the meantime, the tremendous impact on society will happen so be ahead of the curve, be part of the debate, and be proactive in finding equitable solutions.

Ed Sappin is the CEO of Sappin Global Strategies (SGS), a strategy and investment firm dedicated to the innovation economy.

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How AI could create a world of haves and have nots - VentureBeat

Mozilla, the company behind the Firefox internet browser, has begun testing a feature that lets you enter a search query using your voice instead of typing it in. The move could help Mozilla's efforts to make Firefox more competitive with Google Chrome.

If you're using Firefox in English on Mac, Windows or Linux, you can turn on the experimental "Voice Fill" feature and then use it on Google, Yahoo and DuckDuckGo. Support for other websites will come later.

Alphabet's Google offers speech recognition on its search engine when accessed through Chrome on desktop -- it became available in 2013 -- and Yahoo, Microsoft's Bing and Google all let you run search queries with your voice on mobile devices. But searching with your voice on Google while using Firefox on the desktop, for example, has historically been impossible. Now Mozilla wants to make its desktop browser more competitive.

The Voice Fill feature comes a few weeks after Mozilla announced the Common Voice Project that allows people to "donate" recordings of them saying various things in order to build up "an open-source voice recognition engine" that anyone will be able to use. Mozilla will use recordings from Voice Fill and the Common Voice Project in order to make the speech recognition more accurate, speech engineer Andre Natal told CNBC in an interview.

Mozilla's latest efforts follow Facebook's push into speech recognition. And speech technology has become hotter thanks to the rise of "smart" speakers like the Amazon Alexa, the Google Home, and the Apple HomePod. Harman Kardon is now building a speaker that will let people interact with Microsoft's Cortana assistant.

But these big technology companies have collected considerable amounts of proprietary voice data. So while they zig, Mozilla will zag. Mozilla will release to the public its voice snippets from the Common Voice Project later this year. The speech recognition models will be free for others to use as well, and eventually there will be a service for developers to weave into their own apps, Natal said.

"There's no option for both users and developers to use -- something that is both concerned about your privacy and also affordable," Natal said.

That said, Mozilla is following along with the rest of the tech crowd in the sense that the underlying system -- a fork of the Kaldi open-source software -- employs artificial neural networks, a decades-old but currently trendy architecture for training machines to do things like recognize the words that people say.

Mozilla initially explored incorporating speech recognition into the assistant for its Firefox OS for phones, but in 2016 it shifted the OS focus to connected devices, and earlier this year Mozilla closed up the connected devices group altogether.

Today Mozilla has five people working on speech research and a total of about 30 people working on speech technology overall, Natal said. Eventually the team wants to make the technology work in languages other than English.

Mozilla introduced the browser that became Firefox back in 2002. Over the years the nonprofit Mozilla Foundation has received financial support from Google and Yahoo. Mozilla CEO Chris Beard is currently focused on trying to get people to care about the company again, as CNET's Stephen Shankland reported this week. Recent moves include the launch of the Firefox Focus mobile browser and the acquisition of read-it-later app Pocket.

But while Firefox could have roughly 300 million monthly active users, Chrome has more than 1 billion.

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The latest challenge to Google's AI dominance comes from an unlikely place -- Firefox - CNBC

Were seeing a new wave of venture investments in healthtech companies especially those with strong artifical intelligence and machine learning components. Led by some of the worlds largest biopharma companies and tech-focused venture capitalists, these investments are backing efforts to speed drug discovery, improve tests and treatments, and further medical research. For now, most of the investment is focused in the diagnostics/tools (Dx/Tools) sector.A Silicon Valley Bank analysis last month found that 44 venture-backed deals raised $2.2 billion between 2015 and the first half of 2017 for Dx/Tools companies that use AI/ML as part of their underlying technology.

The investors are increasingly diverse:

For our analysis, SVB segmented Dx/Tools into three subsectors: Dx Tests (yes/no test results), Dx/Tools Analytics (actionable data analytics to help direct treatment), and R&D Tools (research equipment and services for biopharma and academia). These deals include multi-$100 million financings for three companies: GRAIL, Guardant Health, and Human Longevity.

Tech-focused and healthcare investors view investments in this new subsector through different lenses.

Tech investors tend to see their AI/ML investments in Dx/Tools as a vehicle for tackling big data in the healthcare arena. When that complex problem is solved, they expect the market will be huge as will the exit opportunities. Thus, tech investors are making early-stage bets. For example, they are banding together in AI/ML platform companies like Atomwise, Cofactor Genomics, Color Genomics, Gingko Bioworks, and Neurotrack.

Healthcare investors typically consider regulatory pathway, reimbursement, revenue ramp, and the acquirer landscape as they evaluate investments. While these investors see much promise in AI/ML technologies, so far they have largely remained on the sidelines. AI/ML represents a new paradigm in healthcare company formation, and these early-stage companies are just beginning to address approval and commercialization, and thus are often considered too early for healthcare investors.

Looking ahead, collaboration among tech and healthcare investors seems natural: It would create an enhanced team to take advantage of technology expertise and experience in healthcare market approval and adoption. To date, there have been limited collaborations, such as Guardant Health.

Valuation remains one of the sticking points. Anecdotally, there are numerous examples of healthcare investors being outbid by tech investors. But as early-stage companies mature, we expect to see more activity by traditional healthcare venture investors.

At this stage, there are several key questions that have yet to be answered:

There will be some big wins in this space, but the next financing rounds will serve as a key indicator of investor confidence. Well likely see an investor mix led by new tech investors and biopharma corporate venture arms. And we also expect large tech companies to invest as they continue to expand their healthcare footprint. Again, how big a role healthcare venture investors will play is uncertain.

On the acquisition side, big biopharma will continue to target AI/ML companies. And large tech companies looking to make further inroads into healthcare (such as Google, Amazon, Apple, Microsoft, and Dell) will not likely pass up opportunities to take a stake in this emerging healthcare sector.

As machine learning and artificial intelligence are rapidly commercialized for healthcare applications, we expect healthcare investing to shift paradigms, leading to new waves of investors and opportunities for promising companies.

Jonathan Norris is Managing Director at Silicon Valley Bank.

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Investors bet big on AI for health diagnostics - VentureBeat

When 16-year-old Kavya Kopparapu wasnt attending conferences, giving speeches, presiding over her schools bioinformatics society, organizing a research symposium, playing piano, and running a non-profit, she worried about what to do with all her free time.

It was June 2016, the summer after her junior year in high school, and Kopparapu was looking for a new project that would use her computer science skills. Her thoughts quickly turned to her grandfather, who lives in a small city on Indias eastern coast.

In 2013 he began showing symptoms of diabetic retinopathy, a complication of diabetes that damages blood vessels in the retina and can lead to blindness. Eventually he was diagnosed and treated, but not before his vision deteriorated. Still, he was lucky: Although treatments such as medication and surgery can stop or even reverse eye damage if the disease is caught early, most patients never receive care.

Kopparapu knows the statistics by heart: Of 415 million diabetics worldwide, one-third will develop retinopathy. Fifty percent will be undiagnosed. Of patients with severe forms, half will go blind in five years. Most will be poor.

The lack of diagnosis is the biggest challenge, Kopparapu says. In India, there are programs that send doctors into villages and slums, but there are a lot of patients and only so many ophthalmologists. What if there were a cheap, easy way for local clinicians to find new cases and refer them to a hospital?

That was the genesis of Eyeagnosis, a smartphone app plus 3D-printed lens that seeks to change the diagnostic procedure from a 2-hour exam requiring a multi-thousand-dollar retinal imager to a quick photo snap with a phone.

Kopparapu and her teamincluding her 15-year-old brother, Neeyanth, and her high school classmate Justin Zhangtrained an artificial intelligence system to recognize signs of diabetic retinopathy in photos of eyes and offer a preliminary diagnosis. She presented the system at the OReilly Artificial Intelligence conference, in New York City, last month.

The device is ideal for making screening much more efficient and available to a broader population, says J. Fielding Hejtmancik, an expert in visual diseases at the National Institutes of Health (NIH). Other research groups, including Googleand Peek Vision, have recently announced similar systems, but Hejtmancik is impressed with the students ingenuity. These kids have put things together in a very nice way thats a bit cheaper and simpler than most [systems designed by researchers]who, by the way, all have advanced degrees!

Kopparapu has always had a scientific mind. Growing up in Herndon, Virginia, she and her brotherbuilt Knex creations, watched MythBusters and Cosmos, and read Scientific American together over breakfast. But she didnt get hooked on computers until she attended a programming workshop run by the National Center for Women and Information Technology. I went home and taught myself Java, HTML, Python, C, she says. My mom had to tear me away from the computer. Id forget to eat.

In high school, she took classes on computer science, then computer vision, then artificial intelligencebut she was troubled to realized that in each class, she was one of only a few girls. She resolved to start an organization to empower girls to pursue computer science. I dont think the problem is a lack of passion, she says. Its more I dont feel like Im good enough. She founded the Girls Computing League, wooed sponsors such as Amazon Web Services and the president of Harvey Mudd College, and now puts on coding workshops for underprivileged kids.

Eyeagnosis began as most endeavors do these days. I googled a lot, Kopparapu says. She also sent a lot of emailsto ophthalmologists, computational pathologists, biochemists, epidemiologists, neuroscientists, physicists, and experts in machine learning. Then she put together a plan.

First her team worked on the diagnostic AI, choosing to use a machine-learning architecture known as a convolutional neural network (CNN). Neural nets are behind the recent explosion in artificial intelligence, including advances in speech recognition, machine translation, and image captioning. They acquire these skills by parsing vast sets of data (millions of photos of cats, say) and looking for patterns of similarity.

CNNs are especially good at classifying images, so its no coincidence that their design resembles the brains visual system. Information passes through hierarchical layers of neurons called nodes; with each layer, the network recognizes ever more abstract features: Pixels become edges become shapes become objects. Its kind of funny that were using a system based on how the retinal system works to diagnose a retinal disease, Kopparapu says.

Rather than build a network from scratch, she chose an off-the-shelf model developed by Microsoft researchers called ResNet-50. But in order to teach the system to recognize an eye disease, she needed training data.

She found that data in the NIHs EyeGene database, which included 34,000 retinal scans. Many of these images, taken under various conditions with different types of cameras, were blurry or poorly exposed. But that was actually a good thing, Kopporapu says. Its very representative of the real-world conditions youd get with using a smartphone.

By August 2016, her team had trained ResNet-50 to spot diabetic retinopathy with the accuracy of a human pathologist. By October, she had made arrangements with Aditya Jyot Eye Hospital, in Mumbai, to test the Eyeagnosis app, which not only detects disease but also highlights blood vessels and microaneurysms in an imagea process that normally involves injecting a fluorescent dye into patients blood. Were trying to make it as easy as possible for an ophthalmologist to look at all that info and say Heres my final diagnosis.

In November, she shipped her first 3D-printed prototype for the systems lens to the hospital. When fitted onto a smartphone, the lens focuses the phones diffuse, off-centered flash to best illuminate a retina. The complete Eyeagnosis system has already been tried on five patients at the hospital, and in each case it made an accurate diagnosis.

Hejtmancik, the NIH expert, notes that theres a long road to clinical adoption. What shes going to need is a lot of clinical data showing that [Eyeagnosis] is reliable under a variety of situations: in eye hospitals, in the countryside, in clinics out in the boonies of India, he says.

Still, Hejtmancik thinks the system has real commercial potential. The only problem, he says, is that its so cheap that big companies might not see the potential for a profit margin. But that affordability is exactly what you want in medical care, in my opinion, he says.

IEEE Spectrums biomedical engineering blog, featuring the wearable sensors, big data analytics, and implanted devices that enable new ventures in personalized medicine.

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An artificial intelligence program correctly identifies 355 more patients who developed cardiovascular disease 1May

Algorithms spot heart failure, lung cancer, and drug candidates sooner 5Apr

Ray Kurzweil, Rodney Brooks, and others weigh in on the future of artificial intelligence 31May

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Gold nanomesh sensors last for a week on the skin without causing inflammation 20Jul

A machine-learning algorithm picked up differences in brain connections in kids as young as six months old 7Jun

John Rogers's stick-on sensors could keep neonates comfortable 22May

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EEG readings of brain activity could help study pain relief treatments for babies who cannot speak 3May

MITs WiGait wall sensor can unobtrusively monitor people for many health conditions based on their walking patterns 1May

Engineered cells implanted in diabetic mice produce insulin on demand 26Apr

Final Frontier Medical Devices takes home $2.6 million for mobile diagnostic device 14Apr

This lab-on-a-glove puts weapons detection on the user's fingertips 30Mar

Cows and people are the large animals that most dominate the environment 29Mar

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This sleek yet rugged sensor measures better and lasts longer 27Feb

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Teenage Whiz Kid Invents an AI System to Diagnose Her Grandfather's Eye Disease - IEEE Spectrum

Over the past couple of weeks, youve likely seen one of your Facebook friends sharing an article about how a pair of AI-driven Facebook chatbots invented their own language in a deviation from what they were originally programmed to do. The gist is this: Facebook created twin AI chat programs to converse with each other (and learn from each other), and they eventually stopped communicating in English and began communicating in a non-English language they invented.

The headlines reporting about this range from dramatized to exploitative, such as the Telegraphs Facebook shuts down robots after they invent their own language, making it sound like these chatbots were conspiring against humanity or posing some other existential threat. A quick look at any comment feed will show you users responding with fear, excitement, and amusement, with lines like And so it begins or references to the works of Isaac Asimov.

But as Facebooks AI Dhruv Batra noted on Monday, AIs have been inventing new ways of communicating with each other for decades, so the news was, in fact, not news. Batra also explained that, contrary to the headlines, the experiment wasnt shut down but simply altered to tweak the linguistic exchange.

Furthermore, there has been no attempt to hide details of the experiment. Everything is out in the open, with all details publicly available on GitHub, allowing other coders replicate the scenario.

The bottom line is this: New robot languages should be the least of our concerns when it comes to AI.

Tech-minded influencers like Elon Musk and Bill Gates have donated significant time and money to explain AI is a threat and figure out new ways to advance it ethically.

These are some of the main areas where we should be focusing our attention:

Weapons. Military drones are already in operation, and autonomous weaponry has been described as the third revolution in warfare. Automated intelligent weaponry puts fewer soldiers in harms way and is far less expensive than other advanced technologies like fighter jets or nuclear materials. For example, current Reaper drones cost about $13 million, compared to $100 million for a fighter jet. In the future, theyll become far cheaper and ubiquitously available in unregulated (and that means incredibly powerful) mobile weapons that anyone can program to kill and destroy (unless we proactively impose security measures).

Control. What may be most important isnt the AI itself, but whos controlling it. Were dealing with forces that have the power to reshape our world, and if theyre monopolized by greedy corporations, power-hungry nations, or even well-intentioned individuals who simply dont know what theyre doing, the technology could easily be abused or misused. Thats why several Silicon Valley influencers and AI researchers have come together to form OpenAI, an initiative to make AI available to the entire world, not just one group of people. The initiative hasnt been cheap, with one top AI researcher reportedly being offered two to three times his market value (which is already more than a starting NFL quarterback).

Growth. The other problem with AI is the pace of its growth, when uncontrolled by limiting parameters. Artificial general intelligence (AGI) has the power to improve exponentially, since it will hypothetically be able to improve itself, and at some point it will surpass the intelligence of its creators. The agricultural age lasted millennia, the industrial age lasted centuries, the information age has lasted decades, and now the age of AI may last mere years; technology growth accelerates rather than progressing linearly, and now were at a point where any further acceleration will leave us woefully unprepared to deal with the consequences (if we take a reactive, rather than a proactive stance). We havent been able to create superintelligence yet, but we already have the processing power in place the Tianhe-2 in China is capable of 34 quadrillion computations per second (cps), far more than the human brain (at 10 quadrillion cps). If we want to wield this computational power responsibly, we need to have a foundation in place to deal with its unrestricted growth. That means understanding the ethics of AI development, how limitations could work, and having safeguards in place in case something goes awry.

There are some legitimate and serious existential concerns about how AI is being used and how it may develop in the future. However, its irresponsible to overly personify the systems being tested or allow headlines to shape our perspectives. Chatbots inventing a new language isnt threatening; its natural, and our attention belongs elsewhere.

Tony Tie is senior search marketer at Expedia. He has previously worked with a number of Fortune 500 companies to improve their online presence. He is also a marketing and entrepreneurship lecturer at various universities.

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New AI languages should be the least of our concerns - VentureBeat

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

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

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

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

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.

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