Category Archives: Ai

Even the most natural-sounding computerized voiceswhether its Apples Siri or Amazons Alexastill sound like, well, computers. Montreal-based start-up Lyrebird is looking to change that with an artificially intelligent system that learns to mimic a persons voice by analyzing speech recordings and the corresponding text transcripts as well as identifying the relationships between them. Introduced last week, Lyrebirds speech synthesis can generate thousands of sentences per secondsignificantly faster than existing methodsand mimic just about any voice, an advancement that raises ethical questions about how the technology might be used and misused.

The ability to generate natural-sounding speech has long been a core challenge for computer programs that transform text into spoken words. Artificial intelligence (AI) personal assistants such as Siri, Alexa, Microsofts Cortana and the Google Assistant all use text-to-speech software to create a more convenient interface with their users. Those systems work by cobbling together words and phrases from prerecorded files of one particular voice. Switching to a different voicesuch as having Alexa sound like a manrequires a new audio file containing every possible word the device might need to communicate with users.

Lyrebirds system can learn the pronunciations of characters, phonemes and words in any voice by listening to hours of spoken audio. From there it can extrapolate to generate completely new sentences and even add different intonations and emotions. Key to Lyrebirds approach are artificial neural networkswhich use algorithms designed to help them function like a human brainthat rely on deep-learning techniques to transform bits of sound into speech. A neural network takes in data and learns patterns by strengthening connections between layered neuronlike units.

After learning how to generate speech the system can then adapt to any voice based on only a one-minute sample of someones speech. Different voices share a lot of information, says Lyrebird co-founder Alexandre de Brbisson, a PhD student at the Montreal Institute for Learning Algorithms laboratory at the University of Montreal. After having learned several speakers voices, learning a whole new speaker's voice is much faster. Thats why we dont need so much data to learn a completely new voice. More data will still definitely help, yet one minute is enough to capture a lot of the voice DNA.

Lyrebird showcased its system using the voices of U.S. political figures Donald Trump, Barack Obama and Hillary Clinton in a synthesized conversation about the start-up itself. The company plans to sell the system to developers for use in a wide range of applications, including personal AI assistants, audio book narration and speech synthesis for people with disabilities.

Last year Google-owned company DeepMind revealed its own speech-synthesis system, called WaveNet, which learns from listening to hours of raw audio to generate sound waves similar to a human voice. It then can read a text out loud with a humanlike voice. Both Lyrebird and WaveNet use deep learning, but the underlying models are different, de Brbisson says. Lyrebird is significantly faster than WaveNet at generation time, he says. We can generate thousands of sentences in one second, which is crucial for real-time applications. Lyrebird also adds the possibility of copying a voice very fast and is language-agnostic. Scientific American reached out to DeepMind but was told WaveNet team members were not available for comment.

Lyrebirds speed comes with a trade-off, however. Timo Baumann, a researcher who works on speech processing at the Language Technologies Institute at Carnegie Mellon University and is not involved in the start-up, noted Lyrebirds generated voice carries a buzzing noise and a faint but noticeable robotic sheen. Moreover, it does not generate breathing or mouth movement sounds, which are common in natural speaking. Sounds like lip smack and inbreathe are important in conversation. They actually carry meaning and are observable to the listener, Baumann says. These flaws make it possible to distinguish the computer-generated speech from genuine speech, he adds. We still have a few years before technology can get to a point that it could copy a voice convincingly in real-time, he adds.

Still, to untrained ears and unsuspecting minds, an AI-generated audio clip could seem genuine, creating ethical and security concerns about impersonation. Such a technology might also confuse and undermine voice-based verification systems. Another concern is that it could render unusable voice and video recordings used as evidence in court. A technology that can be used to quickly manipulate audio will even call into question the veracity of real-time video in live streams. And in an era of fake news it can only compound existing problems with identifying sources of information. It will probably be still possible to find out when audio has been tampered with, Baumann says, but Im not saying that everybody will check.

Systems equipped with a humanlike voice may also pose less obvious but equally problematic risks. For example, users may trust these systems more than they should, giving out personal information or accepting purchasing advice from a device, treating it like a friend rather than a product that belongs to a company and serves its interests. Compared to text, voice is just much more natural and intimate to us, Baumann says.

Lyrebird acknowledges these concerns and essentially issues a warning in the brief ethics statement on the companys Web site. Lyrebird cautions the public that the software could be used to manipulate audio recordings used as evidence in court or to assume someone elses identity. We hope that everyone will soon be aware that such technology exists and that copying the voice of someone else is possible, according to the site.

Just as people have learned photographs cannot be fully trusted in the age of Photoshop, they may need to get used to the idea that speech can be faked. There is currently no way to prevent the technology from being used to make fraudulent audio, says Bruce Schneier, a security technologist and lecturer in public policy at the Kennedy School of Government at Harvard University. The risk of encountering a fake audio clip has now become the new reality, he says.

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New AI Tech Can Mimic Any Voice - Scientific American

For all the potential scientific and cultural advances brought about by artificial intelligence (AI), it is the humble silicon chip that makes it all possible. Sure, there was the confluence of big data, cloud computing, and the right algorithms, but without the underlying CPUs and GPUs, none of this would have occurred.

With artificial intelligence still in its infancy, there are opportunities for investors to benefit from this paradigm shift that melds science and computing. We don't know who the ultimate winner in AI will be, and there will likely be more than one. What we do know, though, is that chips will be there.

Market intelligence firm Tractica estimates that chipset shipments for AI will grow from 863,000 units in 2016 to 41.2 million units annually by 2025. Two companies are currently positioned to benefit the most from the rapid technological innovations brought on by AI, with one more to watch: NVIDIA Corporation (NASDAQ:NVDA), Intel Corporation (NASDAQ:INTC), and Advanced Micro Devices, Inc. (NASDAQ:AMD).

NVIDIA DGX-1 AI supercomputer in a box. Image source: NVIDIA.

NVIDIA leveraged its position in the AI revolution by having the right tool for the job when AI came calling. Its graphics processing units (GPUs) can handle large numbers of basic mathematical calculations simultaneously. The way these chips processed graphics and AI math calculations was strikingly similar and gave NVIDIA a lead in the space. The computationally intense training of AI systems is still primarily the domain of GPUs, and NVIDIA sits firmly in the lead.

In 2016, NVIDIA grew its revenue 38% over the prior year to a record $6.91 billion and increased earnings per share by 138%. In its most recent quarter, data center revenue, where its AI chip business is housed, more than tripled over the prior-year quarter to 14% of the company's revenue. The stock price mirrored its financial performance with the stock up 230% for 2016.

Intel held its "AI Day: Unleashing the Next Wave" to lay out its vision for AI. Image source: Intel.

Intel has designs on the AI space, and the company has made numerous acquisitions that place it squarely at the intersection of several emerging trends.

Altera developed a field programmable gate array (FPGAs), a chip that can be customized or configured by a customer after manufacturing. Microsoft Corporation (NASDAQ:MSFT) deploys these across its Azure cloud server system for the inferencing stage of AI -- performing a function once the AI system has already been trained -- for applications such as image recognition.

Machine learning start-up Movidius produced systems on a chip (SoC) designed for the computer vision systems used by drones and virtual reality headsets that reduced the power consumption of these data-intensive systems. Deep learning start-up Nervana developed an application specific integrated circuit (ASIC) that is central to Intel's Lake Crest chip (aka, Nervana Engine), which is custom-designed and optimized for AI.

Most recently, Intel acquired machine learning and computer vision company Mobileye N.V. (NYSE:MBLY), which developed cameras used in autonomous driving and software that could detect hazards or obstructions that will be central to self-driving car technology.

Each of the chips in Intel's arsenal serves a distinct function, and these acquisitions give the company a stake in a variety of applications that cut a broad path through the AI marketplace. It would be hard to gage the impact of these latest developments on future performance, but given the breadth of applications, Intel will likely thrive.

AMD introduces Radeon Instinct for AI. Image source: AMD.

AMD is playing catch up in the field and recently released an entire line of chips aimed squarely at deep learning AI applications. While it has produced GPUs for years, it only recently developed processors specifically for AI. AMD has a dedicated following among those looking for a "quality on a budget," and it appears to be pursuing the same strategy in AI. There are also indications that its new line of dedicated chips may seek to match or improve upon existing performance across a broad range of AI applications.

AMD gained market share and rode the coattails of its larger rival with its stock price quadrupling over 2016, while its revenue grew 7% to $4.27 billion. It remains to be seen if AMD can affect a market that is currently dominated by NVIDIA. It may be able to create a niche similar to the one it carved out for itself in the gaming market.

While each of these companies offers a different way to play the AI market, it is important to note that the field is still in its infancy, and no one solution works best for every application. There is still the potential that someone will build a better mousetrap that will make any or all of the choices described above obsolete. Such is the nature of investing in emerging technology -- fortunes are made and lost, sometimes overnight.

Teresa Kersten is an employee of LinkedIn and is a member of The Motley Fool's board of directors. LinkedIn is owned by Microsoft. Danny Vena has the following options: long January 2018 $25 calls on Intel. The Motley Fool owns shares of and recommends Nvidia. The Motley Fool recommends Intel. The Motley Fool has a disclosure policy.

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3 Top Chip Stocks Benefiting From AI - Motley Fool

When Bitfusion launched at Disrupt NY 2015, its focus was on helping developers speed up their applications by giving them pre-compiled libraries that made better use of GPUs, FPGAs and other co-processing technologies. That was two years ago. Today, the hottest market for these technologies is intraining deep learning models, something that was barely on the radar when the company launched. Unsurprisingly, though, thats exactly what Bitfusion is focusing on now.

As the company announced today, it has raised a $5 million Series A round led by Vanedge Capital, with participation from new investor Sierra Ventures and existing investors Data Collective, Resonant VC and Geekdom. The company plans to invest this money into strengthening its research and development efforts and to focus on Bitfusion Flex, its new framework-agnostic platform for building and managing AI projects.

Now in beta, Bitfusion Flex essentially aims to give developers a single platform for managing the life cycle of an AI application. Developers get a single dashboard that takes them from development to training, testing and eventually deployment. Under the hood, Flex uses containers to make scaling and moving experiments and models between local machines and the cloud easy, but it also supports deployments on bare metal, too.

Its important to note that Flexs focus isntnecessarily on making the modelling easier. While it does offer an app store-like experience for setting up your framework of choice (no matter whether thats TensorFlow, Torch, Caffe or similar tools), its strength is in managing the infrastructure you need to build and run these applications. Because of this, it neither cares about the framework, nor where you want to deploy the application.

The service offers both a web-based interface to manage this process as well as a command-line interface that, for example, lets you attach remote GPUs to your local laptop during the development phase.

A lot of people who start deep learning projects cant take them beyond the prototype phase, Bitfusion CEO and co-founder Subbu Rama told me. Everybody wants to do deep learning everywhere, but the Global 2000 they dont have enough people. So with Flex, Bitfusionwants toabstract the tedious work of managing infrastructure away so that the data scientists that companies do eventually manage to hire can focus on their applications.

Looking ahead, Bitfusion plans to expand Flex and bring it out of beta in the next few months. The Austin-based company also plans to expand its Silicon Valley presence (though Rama noted that most of the R&D work will still happen in Austin).

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Bitfusion raises $5M for its AI lifecycle management platform ... - TechCrunch

As director of Microsofts Building 99 research lab in Redmond, Washington, Eric Horvitz gave each of his employees a copy of David McCulloughs The Wright Brothers. I said to them, Please read every word of this book, Horvitz says, tapping the table to highlight each syllable.

Horvitz wanted them to read the story of the Wright brothers determination to show them what it takes to invent an entirely new industry. In some ways, his own career in artificial intelligence has followed a similar trajectory. For nearly 25 years, Horvitz has endeavored to make machines as capable as humans.

The effort has required breaking new ground in different scientific disciplines and maintaining a belief in human ingenuity when skeptics saw only a pipe dream. The first flying machines were canvas flapping on a beach, it was a marvel they got it off the ground, says Horvitz. But in 50 summers, youve got a Boeing 707, complete with a flight industry.

Horvitz wants to fundamentally change the way humans interact with machines, whether thats building a new way for AI to fly a coworkers plane or designing a virtual personal assistant that lives outside his office. He will get a chance to further his influence, with his appointment yesterday as head of all of Microsofts research centers outside Asia.

In his new role, Horvitz will harness AI expertise from each labin Redmond, Washington; Bangalore, India; New York City, New York; Cambridge, Massachusetts; and Cambridge, Englandinto core Microsoft products, as well as setting up a dedicated AI initiative within Redmond. He also plans to make Microsoft Research a place that studies the societal and social influences of AI. The work he plans to do, he says, will be game-changing.

Horvitz, 59, has the backing of one of the industrys most influential figures. Microsoft CEO Satya Nadella has spent the last two years rebuilding the company around artificial intelligence. We want to bring intelligence to everything, to everywhere, and for everyone, he told developers last year.

Handing Horvitz the reins to Microsofts research ensures a renewed, long-term focus on the technology.

Horvitz, long a leading voice in AI safety and ethics, has used his already considerable influence to ask many of the uncomfortable questions that AI research has raised. What if, for instance, the machines unconsciously incarcerated innocent people, or could be used to create vast economic disparity with little regard to society?

Horvitz has been instrumental in corralling thinking on these issues from some of techs largest and most powerful companies through the Partnership on Artificial Intelligence, a consortium that is eager to set industry standards for transparency, accountability, and safety for AI products. And hes testified before the US Senate, giving level-headed insight on the promise of automated decision-making, while recommending caution given its latent dangers.

In 2007, Horvitz was elected to a two-year term as president of the Association for the Advancement of Artificial Intelligence (AAAI), the largest trade organization for AI research. Its hard to overstate the groups influence. Find an AI PhD student and ask them whos the most important AI researcher of all time. Marvin Minsky? President from 1981-1982. John McCarthy? President from 1983-1983. Allen Newell? The groups first president, from 1979-1980. You get the picture.

Throughout Horvitzs AAAI tenure, he looked for the blind spots intelligent machines encountered when put into the open world. They have to grapple with this idea of unknown unknowns, he says. Today, we have a much better idea of what these unknowns can be. Even unintentionally biased data powering AI used by law enforcement can discriminate against people by gender or skin color; driverless cars could miss seeing dangers in the world; malicious hackers could try to fool AI into seeing things that arent there.

The culmination of Horvitzs AAAI presidency, in 2009, was a conference held at the famous Asilomar hotel in Pacific Grove, California, to discuss AI ethics, in the spirit of the meetings on DNA modification held at the same location in 1975. It was the first time such a discussion had been held outside academia, and was in many ways a turning point for the industry.

All the people there who were at the meeting went on to be major players in the implementation of AI technology, says Bart Selman, who co-chaired the conference with Horvitz. The meeting went on to get others to think about the consequences and how to do responsible AI. It led to this new field called AI safety.

Since then, the role of AI has become a topic of public concern. Facebooks Mark Zuckerberg has had to answer the very question that Horvitz began a decade ago: Whos responsible when an algorithm provides false information, or traps people within a filter bubble? Automakers in Detroit and their upstart competitors in Silicon Valley have philosophers debating questions like: When faced with fatalities of passengers or pedestrians, who should a driverless car decide to kill?

But there are also unquestionably good uses for AI, and Horvitz arguably spends more time thinking about thoseeven when hes far from the lab.

When I first met Horvitz, he was stepping off the ice at the Kent Valley Ice Centre hockey rink in, about a 30-minute drive south of Building 99. Fresh from an easy 4-1 victory on the ice and wearing a jersey emblazoned with the team name Hackers, he quickly introduced me to teammate Dae Lee, and launched into a discussion of a potential uses for AI. There are 40,000 people who die every year in the hospital from preventable errors, Horvitz said, still out of breath and wearing a helmet. Dae is working with some predictive machine-learning algorithms to reduce those deaths.

Meeting with him the next day, examples abounded: Algorithms that can reduce traffic by optimizing ride-sharing, systems that aim to catch cancer a full stage before doctors based on your search history (the idea being that you might be searching for information about health conditions that indicate early warnings of the disease), and trying to predict the future by using the past.

Horvitz has been chewing on some of these ideas for decades, and hes quick to tell you if a thought isnt yet completely formedwhether hes discussing the structure of an organization hes a member of, or a theory on whether consciousness is more than a sum of its parts (his current feeling: probably not).

In college, Horvitz pursued similar questions, while earning an undergraduate degree in biophysics from Binghamton University in upstate New York. After finishing his degree, he spent a summer at Mt. Sinai hospital in Manhattan, measuring the electric actuation of neurons in a mouse brain. Using an oscilloscope, he could watch the electric signals that indicated neurons firing.

He didnt intend to go into computer software, but during his first year of medical school at Stanford, he realized he wanted to explore electronic brainsthat is, machines that could be made to think like humans. He had been looking at an Apple IIe computer, and realized he had been approaching the problem of human brain activity the wrong way.

I was thinking about this work of sticking glass electrodes to watch neurons would be like sticking a wire into one of those black motherboard squares and trying to infer the operating system, he said.

He was trying to understand organic brains from the outside in, instead of building them from the inside out. After finishing his medical degree, he went on to get a PhD in artificial intelligence at Stanford.

Some of his first ideas for AI had to do directly with medicine. Among those formative systems was a program meant to help trauma surgeons triage tasks in emergency situations by enabling them to quickly discern whether a patient was in respiratory distress or respiratory failure.

But the machines at the time, like the famed Apple IIe, were slow and clunky. They huffed and puffed when making a decision, Horvitz says. The only way for a machine to be able to make a good decision within the allotted time was if the machine knew its limitationsto know and decide whether it could make a decision, or whether it was too late. The machine had to be self-aware.

Self-aware machines have been the fodder for science fiction for decades; Horvitz has long been focused on actually constructing them. Since the rise of companies like Amazon, Google, and Facebookwhich use AI to manage workflow in fulfillment centers or in products like Alexa or search, or to help connect people on social mediamuch research has been focused on building deep neural networks, which have been proven useful for recognizing people or objects in images, recognizing speech, and understanding text. Horvitzs work pinpoints the act of making a decision: How can machines make decisions like expert humans, considering the effects on themselves and the environment, but with the speed and improvable accuracy of a computer?

In his 1990 Stanford thesis, Horvitz described the idea as a model of rational action for automated reasoning systems that makes use of flexible approximation methods and decision-theoretic procedures to determine how best to solve a problem under bounded computational resources.

Well just call it a kind of self-awareness. While the term is often used interchangeably with consciousness, a term that philosophers still argue over, self-awareness can be considered acting after understanding ones limitations. Horvitz makes it clear that self-awareness isnt a light switchits not just on or off, but rather a sea of small predictions that humans make unconsciously every day, and that can sometimes be reverse-engineered.

To see this in action, consider a game that Horvitz worked on in 2009, where an AI agent moderated a trivia game between two people. It would calculate how much time it had to formulate a sentence and speak it, predicting whether it would be socially acceptable to do so. It was a polite bot. In addition, if a third person was seen by the AIs camera in the background, it would stop the game and ask if they wanted to joina small feat for a human, but something completely out of left field for an artificial game show host.

And thats the magic, right? Thats the moment where it goes from just being a system to being alive, says Anne Loomis Thompson, a senior research engineer at Microsoft. When these systems really work, it is magic. It feels like theyre really interacting with you, like some sentient creature.

Outside of Microsoft, Horvitzs interests in AI safety have gone well past the Asilomar conference. Hes personally funded the Stanford 100 Year Study, a look at the long-term effects of artificial intelligence by a cadre of academics with expertise in economics, urban development, entertainment, public safety, employment, and transportation. Its first goal: to gauge the impact of artificial intelligence on a city in the year 2030.

The Partnership on AI, made up of AI leaders from Microsoft, Google, IBM, Amazon, Facebook, and Apple, represents a way for Horvitz to bring the industry together to talk about use of AI for humanitys benefit. The group has recently published its goals, chiefly creating best practices around fairness, inclusivity, transparency, security, privacy, ethics, and safety of AI systems. It has brought in advisors from outside technology, such as Carol Rose from the ACLUs Massachusetts chapter, and Jason Furman, who was US president Barack Obamas chief economic adviser. Horvitz says there are about 60 companies now trying to join.

Despite the potential dangers of an AI-powered world, Horvitz fundamentally believes in the technologys ability to make human life more meaningful. And now hell have an even larger platform from which to share the message.

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Microsoft's new head of research has spent his career building powerful AIand making sure it's safe - Quartz

With giants like Google, Apple, Samsung and Uber in the race, we are likely tobegin spotting driverless vehicles on the road much more often in the years to come. But what is the current state of affairs in the self-driving car industry? This fascinating short documentary will bring you up to date.

Produced by Red Hat Films, Road to AIexplores the future of technology at the intersection between self-driving cars and artificial intelligence. The docufilm is thelatest instalment to the companysOpen Source Stories series thattraces the various ways in which AI has crept into our lives and surroundings.

Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us.

Featuring commentariesfrom AI luminaries like NutonomyCEO Karl Iagnemma, Skymind CEO Chris Nicholson, Google researcherFranois Cholletand Duke University professor Mary Cummings, Road to AItakes a deep look at how AI is paving the way for self-driving cars to reach the masses.

AI will increasingly integral to our lives, to our society. It will become part of our basic infrastructure of society, it will become our interface to the world, to a world that will be increasingly information rich and complex. AI will change what it means to be human, says Chollet.

Building on this thought, Road to AI goes on to speculate it is precisely AI that will save lives on the roads and help autonomous driving tech cement its wayinto mainstream ubiquity.

Road to AI premieres todaywith adebut on two fronts both online and at the Red Hat Summit in Boston. Watch the full documentary in the video section above.

Road to AI on Red Hat

Read next: About.com is reborn as Dotdash

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Watch this documentary about the AI-powered future of self-driving cars - TNW

Forbes

Tinder Has Been Raided For Research Again, This Time To Help AI 'Genderize' Faces Forbes In the age of screen shots and data trails, the idea of putting yourself 'out there' has gained new meaning, especially as dating apps are increasingly mined for users' potentially quite personal info. In a new perceived privacy breach, one developer ... and more »

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Tinder Has Been Raided For Research Again, This Time To Help AI 'Genderize' Faces - Forbes

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What: AI-augmented government: Using cognitive technologies to redesign public sector work, a report by the Deloitte Center for Government Insights that explores how governments can use artificial intelligence to become more efficient.

Why: At a minimum, AI could save 96.7 million federal hours annually, which would mean potential savings of $3.3 billion, Deloitte says.

Findings: AI can increase speed, enhance quality and reduce costs. Some of the possibilities include:

1. Overcome resource constraint: AI is much faster and more accurate at sifting through large volumes of information. The Georgia Government Transparency and Campaign Finance Commission uses handwriting analysis software to speed the processing of 40,000 pages of disclosures it receives every month.

2. Reduce paperwork: The federal government spends a half-billion hours every year on documenting and recording information. Robotics and cognitive automation could perform data entry and paperwork processing in any number of areas -- for child welfare workers, for example, leaving them more time for interaction with children and their families.

3. Cut backlogs: The U.S. Patent and Trademark Offices backlog of patent applications hinders innovation, but cognitive technologies can sift through large data backlogs and perform simple, repetitive actions, leaving difficult cases to human experts. Robotic process automation can automate workflow, in some cases with little human interaction.

4. Enable smart cities: When combined with internet-of-things infrastructure, AI can monitor the surrounding environment to dim street lighting, monitor pedestrian traffic and adjust traffic lights to ease rush hours.

5. Predict outcomes: Machine learning and natural-language processing can spot patterns and suggest responses. Measuring soldiers vital signs with wearable physiological monitors lets the Army predict the seriousness of wounds and prioritize treatment, for example. The Southern Nevada Health District, meanwhile, uses AI to analyze Twitter posts to find restaurants where people reported food poisoning so it can direct investigations to those locations.

6. Answer questions: Automation can offload work from call centers that answer many of the same questions multiple times a day. The Armys SGT STAR virtual assistant, for example, helps recruits understand their different enlistment options, performing the work of 55 recruiters with a 94 percent accuracy rate.

Read the full report here.

About the Author

Matt Leonard is a reporter/producer at GCN.

Before joining GCN, Leonard worked as a local reporter for The Smithfield Times in southeastern Virginia. In his time there he wrote about town council meetings, local crime and what to do if a beaver dam floods your back yard. Over the last few years, he has spent time at The Commonwealth Times, The Denver Post and WTVR-CBS 6. He is a graduate of Virginia Commonwealth University, where he received the faculty award for print and online journalism.

Leonard can be contacted at mleonard@gcn.com or follow him on Twitter @Matt_Lnrd.

Click here for previous articles by Leonard.

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6 ways AI can improve how government works right now - GCN.com

Forbes

AI In Medicine: Rise Of The Machines Forbes Could a robot do my job as a radiologist? If you asked me 10 years ago, I would have said, No way! But if you ask me today, my answer would be more hesitant, Not yet but perhaps someday soon. In particular, new deep learning artificial ...

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AI In Medicine: Rise Of The Machines - Forbes

After announcing that it would shift some of its emphasis away from non-lethal weapons to police body cameras, for a fleeting moment it felt like the company synonymous with sticks that electrocute people was showing an interest in police accountability. Analysis fromthe Intercept and a 2017 Law Enforcement Technology Report by Taser suggest that the reality might be more complicated and considerably creepier.

The company now known as Axon created its body camera division a few years ago, but ramped up efforts in 2017. After acquiring two AI companies, Dextro and Fossil Group, in February, signs point to the fact that the company wants to aim its new machine learning brainpower at policing.

While the company has explicitly denied its interest in building a predictive policing engine, claiming that it will not make predictions on behalf of our customers, the industry report makes plain reference to its desireto automate the collection and analysis of virtually all information in public safety while extracting key insights never before possible. In a page on AI and machine learning, the report lauds the superior insight culled from massive data sets that companies in other industries leverage to predict customer behavior. It continues:

We may not be quite at the Tom Cruise Minority Report level of cognitive prediction, but patterns of individual behavior will become increasingly informative in revealing the probability that an individual will act in a particular fashion. And as our data sets become ever bigger, the analytical algorithms will become ever more sophisticated in revealing robust patterns. It is inevitable that predictive policing will expand. I dont view this to be a bad thing and is consistent with TASERs two principles: protect life; protect truth. Any technology platform that can advance these two laudable goals, while protecting the privacy and rights of innocent citizens should, and indeed must, be adopted.

Considering Tasers significant investments in machine intelligence, providing data to help police forces make life or death decisions certainly sounds within the companys wheelhouse. Exactly how that will play out or if its own newly-founded ethics board will rein in that mission remains to be seen.

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With AI investments, Taser could use its body camera division for predictive policing - TechCrunch

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The Unsettling Performance That Showed the World Through AI's Eyes - WIRED