Category Archives: Ai

If you couldn't feel the love this week, you're lacking a heart.

Tech firms threw AI code and services at devs in a further attempt to secede and ultimately tie them into their respective ways of talking to machines.

Facebook open-sourced its Caffe2 deep-learning framework open-sourcing of code being a proven way in tech of increasing uptake by removing legal and licensing hurdles for the all-important, canary-in-the-coalmine developer demographic that helps drive early adoption and toe-hold acceptance of new tech.

Microsoft updated its Cognitive Services with the addition of three APIs Face, Computer Vision and Content Moderator through its Azure portal. The APIs let computers understand supposedly a picture, to compare faces and group them, and to quarantine images something Facebook might want to tackle given its problems.

Cloud giant AWS sought to leverage the popularity of its parent's digital assistant, Alexa.

Amazon's cloud business released Lex, an artificial intelligence service that lets you build software capable of taking human instruction by voice and text.

Lex employs the artificial speech recognition and natural language understanding capabilities of Amazon's Alexa and in theory - lets devs build applications running on top of AWS's machine-learning framework.

You can, again in theory, build voice and text interaction and understanding without also building the deep, machine-learning framework that must accompany it.

To build their conversational applications, devs would give Lex sample phrases while Amazon's service builds for you the machine-learning models that parse phrases, understand the intent, manage the conversation and produce output.

Like it or not, hype or future fact, AI and ML are big for tech vendors. We are living in times akin to the early years of the personal computer during the 1980s and 1990s, a Gold Rush era with many initial players.

During those years, the competition was whittled down through successful technology, partnership and marketing, through bad leadership, bad ideas and bad execution.

The firm that dominated personal computing offered devs a compelling platform proposition and the right tools.

That firm was Microsoft, the platform was Windows and the tools were, well, dev's tools.

Here we are again.

Then the platform the computer operating system was king and tech firms played to lock devs into theirs through the apps that were built using their tools.

Today, everybody is playing it a lot looser: the platform is no longer a PC operating system, it's a collection of servers and services underpinning each firm's cloud.

The wisdom has it that the apps must be free-ish to roam. It's as self-serving now as it was 30 years ago: to build the biggest and best supported underlying platform; only the apps are following the people.

The apps will suck in the data and the AI and ML interactions made on the endpoints by people. Those endpoints are other tech firms' devices or clouds.

Lex is a platform built on AWS, and you can use it to publish apps for Facebook's Messenger, Slack, Twilio, web applications and IoT devices. Lex handles the authentication and according to AWS scale, thanks to its cloud's elasticity.

You just better hope AWS is having a good day.

AWS this week listed US financial services giants Capital One and Liberty Mutual and VoIP service Vonage as Lex users.

Microsoft Cognitive Services, while built for Redmond's underlying Bot Framework, works on iOS and Android as well as Windows. It would have to given Microsoft's poor showing in mobile and devices and the success of the other two.

And Facebook's worked with chip makers Nvidia, Qualcomm and Intel in addition to Microsoft and Amazon to tune Caffe2 for a variety of mobile devices iOS, Android, Raspberry Pi and Azure and AWS clouds.

If you're a coder, you should feel flattered by the attention. You should get used to it expect more at least while the big names scramble for control.

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The pain in your chest? That'll be Big Tech's AI arrow of love - The Register

Updated 3 hours ago

Museums are often catalogs of the past.

But a new exhibit at the Smithsonian's National Museum of American History will showcase the possible future of cybersecurity.

Mayhem, a cybersecurity bot that uses artificial intelligence to detect and defend against attacks, was put on display Tuesday on the first floor of the Washington museum's innovation wing. Mayhem was built and designed by Pittsburgh-based ForAllSecure.

It's an amazing piece of technology by itself, said Arthur Daemmrich, director of the Lemelson Center for the Study of Invention and Innovation , a think tank inside the National Museum of American History that is geared toward innovation. Mayhem represents not only an innovative approach to cybersecurity, he said, but is also a symbol of the advancements in artificial intelligence.

Mayhem won the Defense Advanced Research Projects Agency's first-ever Cyber Grand Challenge in August. The computer competed against six other computers in 96 rounds of Capture the Flag, a competitive hacking game in which players must protect their system while trying to exploit others. What made the Cyber Grand Challenge unique was that the computers were operating autonomously, detecting and patching holes in their own defenses while watching for intrusions.

The competition showcased a new approach to cybersecurity.

Instead of defending against known viruses, worms or other attacks, Mayhem can scan a system for vulnerabilities and fix them before they are exploited.

We have defense mechanisms that rely on what the hack code is and searching for it, but then whenever someone creates a new one, it's not in one of those libraries, Daemmrich said. You're always behind the curve, but here's an artificial intelligence system that from the start was designed to prevent and find those hacks.

That's a significant step forward.

The museum is displaying Mayhem, a computer box that's lit with LED lights just as it was during the Cyber Grand Challenge. There is a placard in front of the computer explaining Mayhem's significance and a video playing next to it featuring footage from the Cyber Grand Challenge and interviews with ForAllSecure.

Mayhem represents the possibilities for innovation, said Tiffany To, ForAllSecure's COO. This is a technology that is just starting to gain awareness, and I think it will be really interesting to see how it progresses and how it will be used.

Alexandre Rebert, a co-founder and the captain of the Cyber Grand Challenge team, said that while the Cyber Grand Challenge showed that a security system such as Mayhem is possible, he hopes its inclusion in the Smithsonian will spread the word.

Business at ForAllSecure has picked up since the team won the Cyber Grand Challenge. ForAllSecure was founded in 2012 as a Carnegie Mellon University spinoff company. To, of ForAllSecure, said the company has attracted interest from agencies in the federal government, banks and financial institutions and companies that make connected devices to bolster their cybersecurity. Rebert said the company is also building a database of connected devices, the Internet of Things, that includes ForAllSecure's assessment of how secure the products are.

Our commercialization strategy is to make it as useful to as many people as possible, Rebert said.

The future of ForAllSecure aside, Daemmrich wanted to include Mayhem in the museum to spark a conversation about artificial intelligence and its effect on employment. The museum is full of technology that has disrupted the workforce, Daemmrich said. As you walk through the museum's innovation wing to get to Mayhem, you pass a patent model of a pin maker from the 1860s that automated the job of making clothing pins.

That's not the end of employment, Daemmrich said of the pin maker. We're at a historical moment right now where our artificial intelligence is going to open new avenues but also threaten types of works.

RELATED: More data could help predict robots' effects on job market, CMU professor says

Daemmrich said society hasn't been great about transitioning workers displaced by technology into new fields. We need to do better this time, Daemmrich said. Exhibits in the museum, especially Mayhem, aren't included to tell visitors what technology will do to the future but to show people the technology that will affect the future and get them talking about how to manage it.

Mayhem seems to be doing that, Daemmrich said. Not 48 hours after the museum put it on display, it was drawing quite a buzz.

Aaron Aupperlee is a Tribune-Review staff writer. Reach Aupperlee at aaupperlee@tribweb.com or 412-336-8448.

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AI-powered cybersecurity bot from Pittsburgh firm lands at Smithsonian - Tribune-Review

Forbes

Using AI To Manage Uncertainty Forbes While it's tempting to dismiss big data as an over-hyped buzzword, a number of projects have already shown its potential. The past year or so have seen a range of fascinating, and diverse, projects emerge that utilize big data to predict the future ...

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Using AI To Manage Uncertainty - Forbes

Executive Summary

Driven by a fear of losing out, many companies have announced AI-focused initiatives. Unfortunately, most of these efforts will fail. This isnt the first time companies have made this mistake. Back in the late 90s, the big buzz was around the internet. Most companies started online divisions. But there were very few early wins. Then, the dot-com bust happened. A few years later, they were caught napping when online upstarts completely disrupted industries like music, travel, news and video while transforming scores of others. The authors argue thata similar story of early failures leading to irrational retreats will play out with AI. How does a manager justify continuing to invest in AI if the first few initiatives dont produce results?The authors suggest taking a portfolio approach to AI projects a mix of projects that might generate quick wins and long-term projects focused on transforming end to end workflow.

Artificial intelligence is a hot topic right now. Driven by a fear of losing out, companies in many industries have announced AI-focused initiatives. Unfortunately, most of these efforts will fail. They will fail not because AI is all hype, but because companies are approaching AI-driven innovation incorrectly. And this isnt the first time companies have made this kind of mistake.

Back in the late 1990s, the internet was the big trend. Most companies started online divisions. But there were very few early wins. Oncethe dot-com bust happened, these companies shut down or significantly downscaled their online efforts. A few years later they were caught napping when online upstarts disrupted industries such asmusic, travel, news, and video, while transforming scores of others.

In the mid-2000s, the buzz was about cloud computing. Onceagain, several companies decided to test the waters. There were several early issues, ranging from regulatory compliance to security. Many organizations backed off from moving their data and applications to the cloud. The ones that persisted are incredibly well-positioned today, having transformed their business processes and enabled a level of agility that competitors cannot easily mimic. The vast majority are still playing catch-up.

How it will impact business, industry, and society.

We believe that a similar story of early failures leading to irrational retreats will occurwith AI. Already, evidence suggests that early AI pilots are unlikely to produce the dramatic results that technology enthusiasts predict. For example, early efforts of companies developing chatbots for Facebooks Messenger platform saw 70% failure rates in handling user requests. Yet a reversal on these initiatives among large companieswould be a mistake. The potential of AI to transform industries truly is enormous. Recent research from McKinsey Global Institute found that 45% of work activities could potentially be automated by todays technologies, and 80% of that is enabled by machine learning. The report also highlighted that companies across many sectors, such as manufacturing and health care, have captured less than 30% of the potential from their data and analytics investments. Early failures are often used to slow or completely endthese investments.

AI is a paradigm shift for organizations that have yet to fully embrace and see results from even basic analytics. So creating organizational learning in the new platform is far more important than seeing a big impact in the short run. But how does a manager justify continuing to invest in AI if the first few initiatives dont produce results?

We suggest taking a portfolio approach to AI projects: a mix of projects that might generate quick wins and long-term projects focused on transforming end-to-end workflow. For quick wins, one might focus on changing internal employee touchpoints, usingrecent advances in speech, vision, and language understanding. Examples of these projects might be a voice interface to help pharmacists look up substitute drugs, or a toolto schedule internal meetings. These are areas in which recently available, off-the-shelf AI tools, such as Googles Cloud Speech API andNuances speech recognition API, can be used, and they dont require massive investment in training and hiring. (Disclosure: One of us is an executive at Alphabet Inc., the parent company of Google.) They willnot be transformational, but they will help build consensus on the potential of AI. Such projects also help organizations gain experience with large-scale data gathering, processing, and labeling, skills that companies must have before embarking on more-ambitious AI projects.

For long-term projects, one might go beyond point optimization, to rethinking end-to-end processes, which is the area in which companies are likely to see the greatest impact. For example, an insurer could take a business process such as claims processing and automate it entirely, using speech and vision understanding. Allstate car insurance already allows users to take photos of auto damage and settle their claims on a mobile app. Technology thats been trained on photos from past claims can accurately estimate the extent of the damage and automate the whole process. As companies such as Google have learned, building such high-value workflow automation requires not just off-the-shelf technology but also organizational skills in training machine learning algorithms.

As Google pursued its goal of transitioning into an AI-first company, it followed a similar portfolio-based approach. The initial focus was on incorporating machine learning into a few subcomponents of a system (e.g., spam detection in Gmail), but now the company is using machine learning to replace entire sets of systems. Further, to increase organizational learning, the company is dispersing machine learning experts across product groups and training thousands of software engineers, across all Google products, in basic machine learning.

This all leads to the question of how best to recruit the resources for these efforts. The good news is that emerging marketplaces for AI algorithms and datasets,such as Algorithmia and the Google-owned Kaggle,coupled with scalable, cloud-based infrastructure that iscustom-built for artificial intelligence, are lowering barriers. Algorithms, data, and IT infrastructure for large-scale machine learning are becoming accessible to even small and medium-size businesses.

Further, the cost of artificial intelligence talent is coming down as the supply of trained professionals increases. Just as the cost of building a mobile app went from $200,000$300,000in 2010 to less than $10,000 today with better development tools, standardization around few platforms (Android and iOS), and increased supply of mobile developers, similar price deflation in the cost of building AI-powered systems is coming. The implication is that there is no need for firms to frontload their hiring. Hiring slowly, yet consistently, over time and making use of marketplaces for machine learning software and infrastructure can help keep costs manageable.

There is little doubt that an AI frenzy is starting to bubble up. We believe AI will indeed transform industries. But the companiesthat will succeed with AI are the ones that focus on creating organizational learning and changing organizational DNA. And the ones that embrace a portfolio approach rather than concentrating their efforts onthat one big win will be best positioned to harness the transformative power of artificial learning.

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The First Wave of Corporate AI Is Doomed to Fail - Harvard Business Review

Ai Weiwei, the Chinese dissident artist, has launched an attack on eroding freedoms in Hong Kong after he was refused a bank account at HSBC in the former British colony.

The burly artist, who is a constant thorn in the side of Beijings Communist Party rulers, turned to social media to ridicule the one country, two systems principle, which supposedly guarantees freedoms in Hong Kong following its handover to China in 1997.

After he was turned away by the bank, Mr Ai posted a picture of the Hong Kong headquarters of HSBC on Tuesday, saying: Im in Hong Kong, trying to open an account at HSBC. My request was refused due to a commercial decision from the headquarter (sic).

This has not happened to me in Beijing. Maybe one country, one system is better, he said.

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Ai Weiwei criticises Hong Kong freedoms after he is refused for a HSBC bank account - Telegraph.co.uk

Ada, a London and Berlin-based health tech startup, sees its official U.K. push today, and in doing so joins a number of other European startups attempting to market something akin to an AI-powered doctor.

The companys mobile offering bills itself as a personal health companion and telemedicine app and via a conversational interface is designed to help you work out what symptoms you have and offer you information on what might be the cause. If needed, it then offers you a follow up remote consultation with a real doctor over text.

In a call, two of Adas founders CEO Daniel Nathrath and Chief Medical Officer Dr Claire Novorol explained that the app has been six years in the making, and actually started life out as being doctor-facing, helping clinicians to make better decisions. The same database and smart backend is now being offered to consumers to access, albeit with a much more consumer-friendly front-end.

In my brief testing of the app, I plugged in the symptoms of a sore or red eye. After drilling through a quite extensive set of questions, many of which appeared to relate to the answers Id previously given, the Ada app provided three possible conditions, and advised that they could be successfully treated at home.

That, say the companys founders, reflects one of the main benefits of an AI-driven healthcare app like Ada, which is to empower patients to make more informed decisions about their health. Or, to out it more bluntly, to ensure we only visit a doctor when we need to and, more generally, can be proactive in our healthcare without adding the need for greater human doctor resources.

In other words, just like competitor Babylon, which has added its own AI-powered triage functionality and is backed by two of DeepMinds founders, this is about using technology to help healthcare scale.

Ada has been trained over several years using real world cases, and the platform is powered by a sophisticated artificial intelligence (AI) engine combined with an extensive medical knowledge base covering many thousands of conditions, symptoms and findings, explains the company.

In every assessment, Ada takes all of a patients information into consideration, including past medical history, symptoms, risk factors and more. Through machine learning and multiple closed feedback loops, Ada continues to grow more intelligent, putting Ada ahead of anyone else in the market.

With that said, Ada isnt claiming to replace your doctor anytime soon. Like a lot of AI being applied to various verticals, not least healthcare, the app is designed to augment the role of humans, not replace it altogether.

This happens very tangibly in two ways: helping to act as a prescreen consultation before, if needed, being handed off to a real doctor for further advice, or simply helping to create a digital paper trail before a consultation takes place. By getting some of the most obvious symptom-related questions out of the way and captured and analysed by the app, it saves significant time during any follow up consultation.

Novorol tells me that since the app went live, feedback has already shown it to successfully diagnose both common and quite rare conditions. She also talked up the notion that Adas AI, since it has and continues to be trained by real doctors, essentially pools a lot of shared expertise. It did start off as a tool to help doctors avoid misdiagnosis, after all.

I asked how Ada compares to Babylon, and although he slightly comically refused to say the companys name out loud, CEO Nathrath said that unlike competitors, AI isnt an afterthought. Where others have started with a Skype your doctor type offering and added AI, Ada is six years AI in the making and is only now adding remote consultations.

Meanwhile, the startup is being quite secretive regards how it is funded. Aside from an EU grant, Ada Health is said to be backed by unnamed private individuals.

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Ada is an AI-powered doctor app and telemedicine service - TechCrunch

Forbes

Chatbots Go Cha-Ching: The Looming Impact of AI In Finance Forbes From minting coins to dispensing greenbacks on ATMs, the love affair between money and machine goes a long way back. The pervasive influence of technology in how we create, exchange and store money treads a colorful history replete with ...

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Chatbots Go Cha-Ching: The Looming Impact of AI In Finance - Forbes

Forbes

The Great AI Recruitment War: Amazon Is On Top, And Apple Is ... Forbes The top 20 AI recruiters are spending more than $650 million annually to hire talent in this field. and more »

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The Great AI Recruitment War: Amazon Is On Top, And Apple Is ... - Forbes

F8 2017 Facebook has open sourced Caffe2, the toolbox of deep-learning software its own developers use to train AI models and build apps.

Caffe2 appears to be product driven, and is geared towards deploying machine-learning systems into smartphone applications and onto large-scale clusters. It differs from PyTorch, another software framework from Facebook that is more research oriented as it allows programmers to experiment with different neural network architectures more easily.

Using AI in production is tricky, and Caffe2, written in a mix of Python and C++, tries to alleviate the pain.

Facebook has been working with Nvidia to integrate Caffe2 into the graphics chip giant's deep-learning developer libraries so the framework can take advantage of hardware acceleration on Nv's GPUs. We're told, for example, Caffe2 is nippy on Facebooks Big Basin OpenCompute AI servers that pack 64 Nvidia Tesla P100 GPUs. Intel, Microsoft and Amazon have also stepped up to make sure Caffe2 is optimized for their systems and services.

Meanwhile, Qualcomms Neural Processing Engine (NPE) software development kit supports Caffe2 and Googles TensorFlow. This library glues software to the neural network math unit built into its top-end Snapdragon 835 system-on-chip, which is appearing in smartphones, tablets and notebooks this year. Code using the engine will therefore gain a performance boost via the on-chip acceleration. The NPE dev kit will be available in July.

Its a sensible move, considering Qualcomm designs chips used in millions and millions of Android devices, and Facebook is investing heavily in augmented reality, a technology that will manipulate photos and video taken with smartphones. That's going to require some machine-learning processing to identify objects and meddle with them hence the marriage between Caffe2, Qualcomm, and Facebook.

And, hey, if that means developers using Facebook's tech to produce apps to rival SnapChat the image-fiddling toy that's cool with the kids and in the way of Mark Zuckerberg's world domination plans so much the better.

Augmented reality is going to help us mix the digital and physical in all new ways and that's going to make our physical reality better," Facebook CEO Zuckerberg told his social network's F8 conference in San Francisco on Tuesday.

Caffe2 doesn't just add cartoon doodles over images. It's hoping to be more general purpose, allowing developers to create chatbots, hook up IoT devices, use machine translation and speech, and image classification algorithms for medical applications.

Caffe, a predecessor of Caffe2, was developed by Yangqing Jia while he was a PhD student at the University of California, Berkeley. Jia is now a research scientist and leads Facebooks efforts in building a general platform for its AI applications.

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Facebook brews Caffe2 AI toolkit so apps can give SnapChat a slap - The Register

Alphabet's (NASDAQ:GOOGL) (NASDAQ:GOOG) Google announced at its I/O Developers Conference in May 2016 that it had designed a new chip, called the tensor processing unit (TPU), specifically designed for the demands of training artificial intelligence (AI) systems.The company didn't divulge much at the time, butin a blog post that same week, hardware engineer Norm Jouppi revealed that Google had been running the TPU in the company's data centers for more than a year and...

... found them to deliver an order of magnitude better-optimized performance per watt for machine learning. This is roughly equivalent to fast-forwarding technology about seven years into the future (three generations of Moore's Law).

The chip was an application-specific integrated circuit (ASIC), a microchip designed for a specific application. Little else was known about the enigmatic TPU, and the mystery continued until last week, when Google pulled back the curtain to reveal the inner workings of this new groundbreaking advancement for AI.

Google's tensor processing unit could revolutionize AI processing. Image source: Google.

The TPU underlies TensorFlow, Google's open-source machine learning framework, a collection of algorithms that power the company's deep neural networks. These AI systems are capable of teaching themselves by processing large amounts of data. Google tailored the TPU to meet the unique demands of training its AI systems, which had previously been run primarily on graphics processing units (GPUs) manufactured by NVIDIA Corporation (NASDAQ:NVDA). While the company currently runs the TPU and GPU side by side (for now), this could have drastic implications for how AI systems are trained going forward.

Google released a study -- authored by more than 70 contributors --that provided a detailed analysis of the TPU. In a blog post earlier this month, Jouppi laid out the capabilities of the chip. He described how it processed AI production workloads 15 to 30 times faster than CPUs and GPUs performing the same task, and achieved a 30 to 80 times improvement in energy efficiency.

Google realized several years ago that if customers were to use Google voice search for just three minutes each day, that would require the company to double its existing number of data centers. The company also credits the TPU with providing faster response times for search, acting as the linchpin for improvements in Google Translate, and was a key factor in its AI system's defeat of a world champion in the ancient Chinese game of Go.

Companies are taking a variety of approaches to bring improvements to AI systems. Intel Corporation's (NASDAQ:INTC) recently acquired start-up Nervana has developed its own ASIC, the Nervana Engine, that eliminates components from the GPU not essential to the functions necessary for AI. The company also re-engineered the memory and believed it could realize 10 times the processing currently performed by GPUs.Intel is working to integrate this capability on its existing processor platforms to better compete with NVIDIA's offering.

A field-programmable gate array (FPGA) processor can be reprogrammed after installation and is another chip being leveraged for gains in AI. FPGAs have increasingly been used in data centers to accelerate machine learning.Apple Inc. (NASDAQ:AAPL) is widely believed to have installed this chip in its iPhone 7 to promote sophisticated AI advances locally on each phone. The company has emphasized not sacrificing user privacy to make advances in AI, so this would be a logical move for its smartphones.

NVIDIA Tesla P100 powers Facebook's AI server. Image source: NVIDIA.

Facebook, Inc. (NASDAQ:FB) has taken a different approach in optimizing its recently released data center server named Big Basin. The company created a platform that utilizes eight NVIDIA Tesla P100 GPU accelerators attached with NVLink connectors designed to reduce bottlenecks, in what it described as "the most advanced data center GPU ever built." The company revealed that this latest server is capable of training 30% larger machine learning data systems in about half the time.Facebook also indicated that thearchitecture was based NVIDIA's DGX-1 "AI supercomputer in a box."

Though we have been hearing about almost daily breakthroughs in AI, it is important to remember that the science is still in its infancy and new developments will likely continue at a rapid pace. These advances provide for more efficient systems and lay the foundation for future progress in the field. These necessary advances will propel future innovation, but are difficult to quantify in terms of dollars and cents, as well as the potential effects on future revenue and profitability.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Danny Vena owns shares of Alphabet (A shares), Apple, and Facebook. Danny Vena has the following options: long January 2018 $85 calls on Apple, short January 2018 $90 calls on Apple, long January 2018 $640 calls on Alphabet (C shares), short January 2018 $650 calls on Alphabet (C shares), and long January 2018 $25 calls on Intel. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Apple, Facebook, and Nvidia. The Motley Fool recommends Intel. The Motley Fool has a disclosure policy.

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Google's New Chip May Be the Future of AI Systems - The Motley Fool - Motley Fool