The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company.

One of the problems were facing in exploration is the huge increase in the amounts of data we have to look at, said Barnett, in his presentation at theManaging and exploring big data through artificial intelligence and machine learning session at the recent PDAC 2020 convention in Toronto. And although its high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it.

By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits.

But, in an interview with The Northern Miner, he cautioned that to fully exploit the potential of machine learning in mineral exploration, prospectors will still need to devote considerable time and effort to the preparation of data before machine learning techniques can add value for companies.

To illustrate his point, Barnett demonstrated how he and his partner at BW Mining, Peter Williams, are using machine learning to analyze data from geological, geochemical and geophysical surveys of the Yukon in northwestern Canada to locate new deposits.

The Yukon became famous for the Klondike gold rush during the late 1890s, which petered out after a few years as prospectors moved onto Alaska. Today the area is experiencing a renewed interest in what has become known as the Tintina Gold Belt, with significant lode deposits being found over the past two decades and, according to Barnett, more waiting to be discovered.

We used the Yukon bedrock geology map published by the Yukon Geological Survey, which is very detailed and shows over 200 different geological formations, explained Barnett. However, you cant simply put 200 formations into a machine learning process. First, the data requires special treatment.

By representing each of the formations with a separate grid and by continuing the grids upward, they were able to see overlaps between formations, allowing them to consolidate the data by grouping the formations by rock type and age, and thereby reducing the data set down to around 50 discrete and different formations. They then used the same process to represent structural data provided by the map.

The structural data is important because it represents the pathways that the mineralization generally took to reach the surface, said Barnett. We then used geophysical maps of the area provided by Natural Resources Canada, which contain enormous amounts of information that can be extracted and subjected to the same statistical treatment, explained Barnett.

Applying the same approach to geochemical, gravity, topographical and satellite data, they were able to generate detailed data sets covering over 300,000 400,000 square kilometers of the study area.

The most critical layer of data for our machine learning process is the known deposits because this is used to train our artificial neural network against all the other layers to identify deposit formations, said Barnett.

Artificial neural networks operate much like the human brain. They can recognize patterns in the different layers of data and cluster or classify them into groups according to similarities in the input data. They are then capable of discriminating between zones of high and low mineral potential.

After scouring through geologic publications, company websites and NI 43-101 technical reports, Barnett and Williams were able to develop accurate mineral footprints for more than 30 deposits using their model, which, according to Barnett, reportedly contain over 46 million oz. of gold.

They then used an artificial neural network to establish the statistical favourability of a location containing an economically viable deposit across the entire region of interest. This approach is essentially an inversion process that uses exploration data relating to a given location as inputs to the network, which then produces the corresponding favourability as the output.

Image of a typical target. Red areas are highly favourable, while purple areas are shown as unfavourable for gold. Credit: BW Mining.

This requires very sophisticated software to analyze and interpret the data, so you cant just use off-the-shelf software, explained Barnett. We first started analyzing the data on a parallel-processor in the basement of the University of Sussex [in England] back in 1992, where my partner was a professor. But it would take five days to get an answer by which time wed forgotten what the question was.

However, with improvements to computer software and hardware, they are now able to generate an answer in a matter of hours using a common laptop.

Barnetts and Williams use of artificial intelligence and machine learning has led to a highly-focused target map that assigns numerical probabilities of making an economic discovery anywhere in the region of interest. And can be used to systematically rank and rate targets and plan cost-effective follow-up programs that take into account the expected return on investment for any given target.

Although Barnett believes there is currently a lack of understanding of artificial intelligence and machine learning in the industry, he is convinced that as these techniques become more widely used and available, machine learning and artificial intelligence will lead to a wave of discoveries. And within ten years, they will be commonly used tools in the mineral exploration industry.

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Artificial intelligence, machine learning primed to deliver 'a wave of discoveries' - The Northern Miner

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The Artificial Intelligence could carry out attacks without human supervision, a breakthrough technology and also a great risk

The project called CLAWS is directed by the Office of Naval Research, which is responsible for the science and technology programs of the United States Navy and Marine Corps.

Budget documents discovered by New Scientist describe CLAWS as an autonomous system of unmanned submarine weapons that could be installed in underwater robots such as the Orca submarine vehicle developed by Boeing. which can be armed with 12 torpedo tubes that could be controlled by CLAWS without any human intervention.

It will clandestinely extend the reach of large UUVs [ unmanned underwater vehicles] and increase mission areas for kinetic purposes, the documents read .

CLAWS was first revealed in 2018 as an attempt to improve the autonomy and survival of large and extra-large unmanned underwater vehicles, however, until now the capacity of the weapons had not been mentioned, according to New Scientist.

Lethal autonomous weapons

Budget documents reveal that CLAWS has been allocated USD 26 million this year and another 23 million for the next, and it is known that they will be used to develop the idea in a functional submarine prototype .

Stuart Russell, a professor of computer science at the University of California, who described CLAWS as a dangerous development, explained: Equipping a fully autonomous underwater vehicle with lethal weapons is a significant step, and one that runs the risk of accidental climbing in a way that does not apply to marine mines.

Chinese scientists hope to deploy unmanned military submarines in the worlds oceans in the early 2020s, although the final decision on whether to attack will still be taken by commanders, for now the developments suggest that a new front is opening in the career arms of IA at sea.

The rush to develop autonomous and lethal underwater weapons is becoming a growing concern for activists such as the Campaign to Stop Killer Robots, which has attracted the support of technological luminaries such as Elon Musk of Tesla and Mustafa Suleyman of Alphabet, are backed by German Foreign Minister Heiko Maasban, who urged states to ban totally autonomous weapons , before its too late!

Source: La Verdad Noticias

Marketing manager and co-Chief Editor of Maritime Herald.

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Artificial Intelligence Controlled Submarines Are Developed By The US Navy - Maritime Herald

ATLANTA, March 11, 2020 (GLOBE NEWSWIRE) -- Jvion, a leader in Clinical Artificial Intelligence (AI), today announced the release of its innovative Social Determinants of Health (SDOH) solution that identifies socioeconomic barriers driving an individuals health risk and opportunities for investment in community benefit programs to address gaps in care. Leveraging Jvions peer-reviewed analytics layer and Microsoft Azure Maps, the solution empowers providers and health systems to address underserved populations and inequalities in existing healthcare delivery. Jvion goes beyond helping providers better understand the impact of SDOH by offering individualized interventions that aid in aligning community benefits more effectively.

Providers and healthcare executives recognize the growing role of socioeconomic insights in healthcare, especially in meeting the needs of underserved populations. To date, capturing that data and turning it into meaningful and actionable intelligence has proved elusive for many, said Shantanu Nigam, CEO of Jvion. Our unique approach turns socioeconomic, environmental, and behavioral data into real clinical value that drives higher engagement, more tailored interventions, and greater alignment between need and risk, resulting in better outcomes for individuals and the community as a whole.

As alignment and access to community benefit programs continue to be the cornerstone of building healthier communities, providers need appropriate insight into their populations and individual healthcare needs. Hospitals spent $95 billion on community benefits in the most recent year data is available (American Hospital Association), and increasingly both federal and state regulators are seeking clarity on what benefits are being provided to communities with this spend and their impact. Jvions SDOH solution not only fulfills the federal and state assessment needs for healthcare organizations, but also strategically informs providers where to allocate their community benefit spend to have the greatest level of impact.

Jvions SDOH solution requires limited input from providers and none from patients, largely relying on its high-performing AI approach, which leverages a global instance of de-identified patients to power the inferential outputs of the solution. Through this approach, the community inherits the attributes of the individual versus traditional methods, which apply community qualities to the individual. The SDOH solution features an interactive map interface built using Microsoft Azure maps and a web-based portal.

Were pleased that this technology collaboration is helping healthcare organizations in transforming patient care and their businesses. The Microsoft platform helps responsibly unify people, devices, apps and information by prioritizing compliance, security and trust, said Gareth Hall, director of business strategy for Worldwide Healthcare at Microsoft. Our partners are critical in helping healthcare organizations use technology to address industry challenges and seize opportunities to impact peoples lives in a positive way. The combination of the Microsoft platform and partner innovation is key to helping our industry transform.

The Jvion SDOH solution is now available. Register here to schedule a virtual demo. Additional information is available at https://jvion.com/jvionclinicalai/.

The latest KLAS report, Healthcare AI 2019 - Actualizing the Potential of AI, recognized Jvion as having by far the largest client base in the healthcare AI market," and "the largest offering of pre-built healthcare content for machine learning models/vectors." Additionally, Jvion was featured in the CB Insights Digital Health 150, showcasing the most promising private digital healthcare companies in the world.

About JvionJvion enables healthcare organizations to prevent avoidable patient harm and lower costs through its AI-enabled prescriptive analytics solution. An industry first, the Jvion Machine goes beyond simple predictive analytics and machine learning to identify patients on a trajectory to becoming high risk and for whom intervention will likely be successful. Jvion determines the interventions that will more effectively reduce risk and enable clinical action. And it accelerates time to value by leveraging established patient-level intelligence to drive engagement across hospitals, populations, and patients. To date, the Jvion Machine has been deployed across about 50 hospital systems and 300 hospitals, who report average reductions of 30% for preventable harm incidents and annual cost savings of $6.3 million. For more information, visit http://www.jvion.com.

Jvion PR Contact:Lexi Herosianlexi@scratchmm.com

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Jvion Tackles Socioeconomic Barriers to Care with Industry-Leading Artificial Intelligence - GlobeNewswire

MOUNTAIN VIEW, Calif., March 11, 2020 /PRNewswire/ --

Highlights:

Synopsys, Inc.(NASDAQ:SNPS) today announced a major breakthrough in electronic design technology with the introduction of DSO.ai (Design Space Optimization AI), the industry's first autonomous artificial intelligence application for chip design. Inspired by DeepMind's AlphaZero that mastered complex games like chess or Go, Synopsys' DSO.ai solution is an artificial intelligence and reasoning engine capable of searching for optimization targets in very large solution spaces of chip design. DSO.ai revolutionizes chip design by massively scaling exploration of options in design workflows while automating less consequential decisions, allowing SoC teams to operate at expert levels and significantly amplifying overall throughput.

"As new silicon technologies are testing the limits of physics, our customers are looking for manufacturing solutions that enable their innovative products," said Jaehong Park, executive vice president of Foundry Design Platform Development at Samsung Electronics. "In our design environment, Synopsys' DSO.ai systematically found optimal solutions that exceeded our previously achieved power-performance-area results. Furthermore, DSO.ai was able to achieve these results in as few as 3 days; a process that typically takes multiple experts over a month of experimentation. This AI-driven design methodology will enable Samsung Foundry customers to fully utilize the benefits of our cutting-edge silicon technologies for their SOC designs."

Developed from the ground up at Synopsys, DSO.aiis part of a multiyear, company-wide initiative and strategic investment in AI-based design technology.

Chip Design: A Vast Search Space

Today, AI can interact with humans through natural language, identify bank fraud and protect computer networks, drive cars around city streets, and play intelligent games like chess and Go. Chip design too is a very large space of potential solutions, trillions of times larger than, for example, the game of Go.

Searching this vast space is a very labor-intensive effort, typically requiring many weeks of experimentation, and often guided by past experiences and tribal knowledge. A chip design workflow typically consumes and generates terabytes of highly dimensional data compartmentalized and fragmented across many separately optimized silos. To create an optimal design recipe, engineers have to ingest volumes of high-velocity data and make complex decisions on the fly with incomplete analysis, often leading to decision fatigue and over-constraining of their design.

With today's hypercompetitive markets and stringent silicon manufacturing requirements, the difference between a good recipe and an optimal recipe can be 100s of MHz of performance, hours of battery life, and millions of dollars in design costs.

The EDA Industry's First Autonomous AI Application for Chip Design

Synopsys' DSO.ai solution revolutionizes the process of searching for optimal solutions by enabling autonomous optimization of broad design spaces. DSO.ai engines ingest large data streams generated by chip design tools and use them to explore search spaces, observing how a design evolves over time and adjusting design choices, technology parameters, and workflows to guide the exploration process towards multi-dimensional optimization objectives. DSO.ai uses cutting-edge machine-learning technology invented by Synopsys R&D to execute searches at massive scale, autonomously operating tens-to-thousands of exploration vectors and ingesting gigabytes of high-velocity design analysis data all in real-time.

At the same time, DSO.ai automates less consequential decisions,like tuning tool settings, relieving designers of menial tasks and allowing teams to operate at a near-expert level. Knowledge is shared and applied with high effectiveness across entire design teams. This level of productivity means that engineers are now available for more projects, apply more time on a given problem to achieve better results, handle larger parts of a project, and focus on creative and value-added tasks.

A Leap in Productivity

"Ever since the introduction of Design Compiler in the late '80s, Synopsys has been enabling silicon innovators with tools and technologies across the design spectrum," said Sassine Ghazi, general manager, Design Group at Synopsys. "With DSO.ai, once again, Synopsys is starting a new chapter in semiconductor design. More than two years ago we set out on a fascinating journey to bring AI to chip design, partnering with academic researchers, industry thought leaders, and AI technology pioneers. Today's announcement marks a very important milestone, and our journey in AI is only just beginning."

Synopsys' DSO.ai solution is currently in select deployments with industry-leading partners with broader availability planned for the second half of 2020.

About Synopsys

Synopsys, Inc. (NASDAQ:SNPS) is the Silicon to Software partner for innovative companies developing the electronic products and software applications we rely on every day. As the world's 15th largest software company, Synopsys has a long history of being a global leader in electronic design automation (EDA) and semiconductor IP and is also growing its leadership in software security and quality solutions. Whether you're a system-on-chip (SoC) designer creating advanced semiconductors, or a software developer writing applications that require the highest security and quality, Synopsys has the solutions needed to deliver innovative, high-quality, secure products. Learn more at http://www.synopsys.com.

Editorial Contact:Simone Souza Synopsys, Inc. 650-584-6454simone@synopsys.com

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Synopsys Advances State-of-the-Art in Electronic Design with Revolutionary Artificial Intelligence Technology - Benzinga

Leading Artificial Intelligence Company machineVantage Deploys AI and Neuroscience to Identify Highly Effective Means For Public Communications Regarding Covid-19

BERKELEY, Calif., March 11, 2020 /PRNewswire/ --As the global Coronavirus pandemic spreads, one of the major challenges that governments, public health institutions, businesses, and the public face is how to communicate most effectively about actions to take regarding the disease.

MachineVantage (PRNewsfoto/MachineVantage)

Applying artificial intelligence and machine learning systems, combined with advanced neuroscience knowledge, leading AI company machineVantage (www.machinevantage.com) has identified a highly effective communication model to address the international health crisis. The firm specializes in extracting metaphors that connect deeply with the non-conscious mind, which is where over 95% of daily decisions are made.

"Neuroscience teaches us that metaphors are the 'language of the non-conscious mind', and they represent a very powerful method of communicating critical information," said Dr. A. K. Pradeep, founder and CEO of machineVantage. "They are essentially a form of 'shorthand' for the brain, which assigns a high priority to this form of information. By applying customized AI-powered algorithms, accessing a vast library of existing metaphors, and relying on neuroscientific learnings, we are able to extract the most meaningful and impactful new metaphor to use in addressing the Coronavirus crisis."

"That metaphor is 'Health connects to Gateways," Dr. Pradeep said. "We rank metaphors in four levels, and our AI systems isolated this Gateway metaphor as 'Emergent'meaning it is gaining importance in the non-conscious mind. We wish to make this finding universally available as a means of doing our part to help in the struggle against this disease by facilitating better communication to the public."

Dr. Pradeep explained that the non-conscious mind connects Health and Gateways in many ways.

Two primary ways are concepts embedded in the Gateway Metaphor:

A. Health is a Gateway to a better life and to things that matterB. Gateways to Health passageways that enable Health, and preserve being Healthy

Both are activated in the scenario that the destruction of our Health closes gateways to a better future, and Gateways need to be closed to help us be Healthy and remain Healthy in the presence of the Covid-19 virus.

Gateways are typically perceived as physical structures, such as doors, iron gates, or bridges. In the non-conscious mind, Gateways are conceptualized as metaphoric portals, allowing or preventing access. A virus such as Covid-19 as an enemy activates the Gateway Metaphor in the non-conscious mind.

Dr. Pradeep identified six key messaging concepts that the Gateway Metaphor prompts:

"Understanding how the non-conscious mind processes and responds to Coronavirus-related information through the lens of this Gateway Metaphor provides important direction on how to construct and convey messages to the public about this disease," said Dr. Pradeep.

Retail data regarding consumer buying patterns confirm the activation of the Gateway Metaphor in the non-conscious mind. The stockpiling of basic consumables such as toilet paper and canned soup shows the activation of "gates may be closed for awhile". The collection of entertainment items such as games, CDs, and DVDs indicates that "the wait inside may be stressful" is also activated in the non-conscious.

Story continues

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Artificial Intelligence Unlocks "Gateway" Metaphor to Aid the Public, Policy Makers, and Companies in Addressing the Coronavirus Crisis -...

LOS ANGELES, March 11, 2020 /PRNewswire/ --Neil Sahota, a leading artificial intelligence (AI) expert, called today for creation of a new ecosystem to encourage partnerships that could provide a solution to the coronavirus and possibly future pandemic diseases.

Sahota, author of the book Own the A.I Revolution (McGraw-Hill) and anIBM Master Inventor who led the IBMWatson Group, called onfellow researchers, clinicians, doctors, and scientists to "collaborate, treat, and hopefully, cure this disease." He said some big tech companies are "already providing chat and video conferencing tools to support their work, but there is an opportunity for deeper collaboration, perhaps through an ecosystem dedicated to coronavirus and potential future pandemic diseases."

The United Nations AI for Goodadvisor noted that important new perspectives to the effects of the devastating 2010 Deepwater Horizon oil spill were developed through teamwork by environmental scientists, technologists, mechanical engineers and other experts.

"Why not find similar opportunities to create multi-disciplinary teams to combat the coronavirus?" Sahota asks. "Right now, we face two challenges. First, intense time pressure leads to a silo-mentality and we don't recognize there might be more efficient solutions. Second, we may hope for a 'magic bullet' solution with technology like artificial intelligence. But, AI needs lots of data and training, and teamwork is essential.

"That's why a new ecosystem might be effective. Consider the United Nations ITU AI for Good initiative that united government agencies, industry, academia and others creates new solutions for Sustainable Development Goals using AI. The combination of different perspectives, domain knowledge and approaches create a unique synergy that accelerates usable solutions. It could be a very powerful approach for us in fighting the corona virus and future, potential pandemic diseases."

AboutNeil Sahota:Neil Sahotais a futurist and leading expert on Artificial Intelligence (AI) and other next generation technologies. He is the author ofOwn the AI Revolution(McGraw Hill) and works with the United Nations on theAI for Goodinitiative. Sahota is also an IBM Master Inventor, former leader of the IBM Watson Group and professor at theUniversity of California/Irvine. His work spans multiple industries, including legal services, healthcare, life sciences, retail, travel, transportation, energy, utilities, automotive, telecommunications, media, and government.

SOURCE Neil Sahota

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Artificial Intelligence Authority Neil Sahota Calls For Creation Of A New Ecosystem Of Experts To Find Solution To Fight Coronavirus - PRNewswire

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In 1928, a Scottish scientist named Sir Alexander Fleming left his lab where he was studying the staphylococcus bacteria to go on a two-week vacation with his family. When he returned to his lab bench, he not only realized he hadn't tidied his work space very well, but that the dishes with the bacteria in them were growing mold. He also noticed that the bacteria seemed to be actively avoiding the moldy areas of the petri dish. Later he said "I certainly didn't plan to revolutionize all medicine by discovering the world's first antibiotic, or bacteria killer. But I suppose that was exactly what I did."

These days it doesn't take a slovenly scientist to discover important new antibiotics it just takes a computer. A group of researchers at the Massachusetts Institute of Technology (MIT) have used artificial intelligence (AI) to identify a new antibiotic that kills even some hitherto antibiotic-resistant strains.

But does this mean they staffed the lab with robots rather than people? Nope! The research team created a computer model that systematically screened more than a hundred million chemical compounds in just a few days a feat that would take lab technicians many years (and a lot of the same sort of scientific serendipity that visited Fleming) to accomplish.

Very few new antibiotics have been discovered in the past decade, during which time bacteria are getting tougher.

"We're facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anemic pipeline in the biotech and pharmaceutical industries for new antibiotics," said James Collins, a professor in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, in a press release.

The research team developed a machine-learning computer model that could identify about 2,500 molecular compounds that prohibited the growth of bacteria in this case, E. coli, specifically. They then introduced the program to 6,000 drugs that are currently being studied to see if any of them might be useful in curing known human diseases. Once the model selected the molecule with the strongest antibacterial potential that didn't look similar to any known antibiotics, the team used a different model to see if the molecule would be detrimental to people.

Et voila! The model narrowed the candidates down to one the researchers have dubbed it "halicin" which has been tested in the past as a drug to treat diabetes. Halicin has been tested on lab samples of several different antibiotic-resistant strains of bacteria and has been shown to kill almost all of them, with the exception of one very stubborn lung pathogen.

After discovering halicin, the research team used the model to identify 23 more candidates using another database of compounds and found two that were particularly powerful. The researchers are now working to find antibiotics that are more selective in the bacteria they kill, so they don't destroy all our beneficial gut flora while saving our lives. As for halicin, the researchers plan to work with a pharmaceutical company or nonprofit to develop the drug for use in humans, according to the press release.

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Artificial Intelligence Discovers Antibiotic in Record Time - HowStuffWorks

WASHTENAW COUNTY, MI -- The University of Michigan and Eastern Michigan University are each hosting events this month that will focus on the advancement of artificial intelligence.

The University of Michigan Artificial Intelligence Laboratory has been hosting regular Friday Night AI events since May 2019. This weeks free event is scheduled for March 13 at 7 p.m. Organizers say they look to make the events accessible to the general public, so it is hosted at the Ann Arbor District Library downtown in the multi-purpose room.

The intent of this event is to reach out to the community and have a conversation that goes both ways, said Rada Mihalcea, director of U-M AI Lab. Its for the community to learn more about what researchers in the AI Lab at Michigan are working on, and also for us to learn more about the community and whats on their minds and what are their interests.

Fridays theme will dig into Technologies that Power Todays Chatbots, focusing on Amazon Alexa. The University of Michigan AI Lab will discuss its current Alexa Prize Competition project, in which participants are challenged to develop the chatbot to have a 20-minute conversation.

Presenters include professors Nikola Banovic and David Jurgens, along with graduate student Chung Hoon Hong. Registration is encouraged via the Friday Night AI website.

The Department of Mathematics & Statistics at Eastern Michigan University will be hosting the 2020 Machine Learning Conference on March 28. The conference will run from 8:30 a.m. to 4:00 p.m. at the Pray Harrold Building, room 201.

Presenters will discuss two topics: theoretical machine learning and applications, organizers said.

This conference mainly deals with applications because people like to see how artificial intelligence and machine learning can be applied to their real life, said Ovidiu Calin, mathematics and statistics professor.

Graduate students and faculty will join representatives from Ford Motor Company, General Motors, the University of Michigan, Soothsayer Analytics, OML Alpha, LLC and Detroit Autonomous Vehicle Group, who will present during the conference.

Some of the projects to be discussed involve modifications to the 1970s electronic game console Atari, and autonomous vehicle technology.

Registration with a meal is open until March 10, with the option to still register after the meal deadline.

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Atari, Alexa and artificial intelligence to be discussed in UM, EMU events this month - MLive.com

Artificial intelligence has reached the inflection point where its less of a trend than a core ingredient across virtually every aspect of computing. These companies are applying the technology to everything from treating strokes to detecting water leaks to understanding fast-food orders. And some of them are designing the AI-ready chips that will unleash even more algorithmic innovations in the years to come.

For enabling the next generation of AI applications with its Intelligent Processing Unit AI chip

As just about every aspect of computing is being transformed by machine learning and other forms of AI, companies can throw intense algorithms at existing CPUs and GPUs. Or they can embrace Graphcores Intelligence Processing Unit, a next-generation processor designed for AI from the ground up. Capable of reducing the necessary number crunching for tasks such as algorithmic trading from hours to minutes, the Bristol, England, startups IPUs are now shipping in Dell servers and as an on-demand Microsoft Azure cloud service.

Read more about why Graphcore is one of the Most Innovative Companies of 2020.

For tutoring clients like Chase to fluency in marketing-speak

Ever tempted to click on the exciting discount offered to you in a marketing email? That might be the work of Persado, which uses AI and data science to generate marketing language that might work best on you. The companys algorithms learn what a brand hopes to convey to potential customers and suggests the most effective approachand it works. In 2019, Persado signed contracts with large corporations like JPMorgan Chase, which signed a five-year deal to use the companys AI across all its marketing. In the last three years, Persado claims that it has doubled its annual recurring revenue.

For becoming a maven in discerning customer intent via messaging apps

We may be a long way from AI being able to replace a friendly and knowledgeable customer-service representative. But LivePersons Conversational AI is helping companies get more out of their human reps. The machine-learning-infused service routes incoming queries to the best agent, learning as it goes so that it grows more accurate over time. It works over everything from text messaging to WhatsApp to Alexa. With Conversational AI and LivePersons chat-based support, the companys clients have seen a two-times increase in agent efficiency and a 20% boost in sales conversions compared to voice interactions.

For catalyzing care after a patients stroke

When a stroke victim arrives at the ER, it can sometimes be hours before they receive treatment. Viz.ai makes an artificial intelligence program that analyzes the patients CT scan, then organizes all the clinicians and facilities needed to provide treatment. This sets up workflows that happen simultaneously, instead of one at a time, which collapses how long it takes for someone to receive treatment and improves outcomes. Viz.ai says that its hospital customer base grew more than 1,600% in 2019.

For transforming sketches into finished images with its GauGAN technology

GauGAN, named after post-Impressionist painter Paul Gauguin, is a deep-learning model that acts like an AI paintbrush, rapidly converting text descriptions, doodles, or basic sketches into photorealistic, professional-quality images. Nvidia says art directors and concept artists from top film studios and video-game companies are already using GauGAN to prototype ideas and make rapid changes to digital scenery. Computer scientists might also use the tool to create virtual worlds used to train self-driving cars, the company says. The demo video has more than 1.6 million views on YouTube.

For bringing savvy to measuring the value of TV advertising and sponsorship

Conventional wisdom has it that precise targeting and measuring of advertising is the province of digital platforms, not older forms of media. But Hives AI brings digital-like precision to linear TV. Its algorithms ingest video and identify its subject matter, allowing marketers to associate their ads with relevant contentsuch as running a car commercial after a chase scene. Hives Mensio platform, offered in partnership with Bain, melds the companys AI-generated metadata with info from 20 million households to give advertisers new insights into the audiences their messages target.

For moving processing power to the smallest devices, with its low-power chips that handle voice interactions

Semiconductor company Syntiant builds low-power processors designed to run artificial intelligence algorithms. Because the companys chips are so small, theyre ideal for bringing more sophisticated algorithms to consumer tech devicesparticularly when it comes to voice assistants. Two of Syntiants processors can now be used with Amazons Alexa Voice Service, which enables developers to more easily add the popular voice assistant to their own hardware devices without needing to access the cloud. In 2019, Syntiant raised $30 million from the likes of Amazon, Microsoft, Motorola, and Intel Capital.

For plugging leaks that waste water

Wint builds software that can help stop water leaks. That might not sound like a big problem, but in commercial buildings, Wint says that more than 25% of water is wasted, often due to undiscovered leaks. Thats why the company launched a machine-learning-based tool that can identify leaks and waste by looking for water use anomalies. Then, managers for construction sites and commercial facilities are able to shut off the water before pipes burst. In 2019, the companys attention to water leaks helped it grow its revenue by 400%, and it has attracted attention from Fortune 100 companies, one of which reports that Wint has reduced its water consumption by 24%.

For serving restaurants an intelligent order taker across app, phone, and drive-through

If youve ever ordered food at a drive-through restaurant and discovered that the items you got werent the ones you asked for, you know that the whole affair is prone to human error. Launched in 2019, Interactions Guest Experience Platform (GXP) uses AI to accurately field such orders, along with ones made via phone and text. The technology is designed to unflinchingly handle complex custom ordersand yes, it can ask you if you want fries with that. Interactions has already handled 3 million orders for clients youve almost certainly ordered lunch from recently.

For giving birth to Kai (born from the same Stanford research as Siri), who has become a finance whiz

Kasisto makes digital assistants that know a lot about personal finance and know how to talk to human beings. Its technology, called KAI, is the AI brains behind virtual assistants offered by banks and other financial institutions to help their customers get their business done and make better decisions. Kasisto incubated at the Stanford Research Institute, and KAI branched from the same code base and research that birthed Apples Siri assistant. Kasisto says nearly 18 million banking customers now have access to KAI through mobile, web, or voice channels.

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The 10 most innovative artificial intelligence companies of 2020 - Fast Company

The American Board of Artificial Intelligence in Medicine (ABAIM) is pleased to announce its recent incorporation as a not-for-profit entity. The ABAIM plans to credential healthcare professionals and anyone else who seeks a greater understanding of the growing role and use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) solutions in health care.

Spearheading this effort is Orest Boyko, MD, PhD, Associate Professor (Research) of Psychology at The USC Michelson Center for Convergent Bioscience Bridge Institute, located at the University of Southern California; and Anthony Chang, MD, Chief Intelligence and Innovation Officer at the Sharon Disney Lund Medical Intelligence and Innovation Institute (MI3) at Childrens Hospital of Orange County, Orange, CA. Dr. Chang is also the author of an upcoming book entitled Intelligence-Based Medicine: Principles and Applications of Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare and is the Editor-in-Chief of Intelligence-Based Medicine, an open access peer-review journal, published by Elsevier. Both are actively involved in the development and application of AI, ML, and DL in medicine.

Co-chaired by Dr. Boyko and Dr. Chang, ABAIMs growing board comprises professionals from an array of multidisciplinary and institutional backgrounds. These currently include: Matthew Lungren, MD, Assistant Professor of Radiology, Stanford University Medical Center; Kevin Maher, Professor of Pediatrics, Emory University School of Medicine; Spyro Mousses, PhD, CEO, Systems Oncology; Tanveer Syeda-Mahmood, PhD, IBM Fellow & Chief Scientist, IBM Research; Sharief Taraman, MD, Health Science Associate Professor, UC Irvine School of Medicine; and Dennis Wall, PhD, Associate Professor of Pediatrics, Psychiatry, and Biomedical Data Science, Stanford Medical School.

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Dr. Chang continued, The birth of the ABAIM is a tremendously exciting and major milestone in bringing AI education and certification to all healthcare providers. The ABAIMs modular curriculum will enable a wide range of healthcare professionals, including clinicians, executives, technicians, and nurses, as well as patients, data scientists, and information technology personnel, to become knowledgeable in the application of AI, ML, and DL tools and technologies. Our vision of creating synergy between healthcare and AI-related technologies will bring about a new paradigm of intelligence-based medicine and transform health care for everyone.

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American Board of Artificial Intelligence in Medicine aims to educate and certify healthcare professionals in AI - DOTmed HealthCare Business News