Artificial Intelligence Used to Analyze Opinions Through Brain Activity – Unite.AI

Chris Aimone co-founded Muse with an ethos to create technology that expands our perspective of ourselves and the world around us.

An artist and inventor at heart, Chris creative and design practice has spanned many fields, including architecture, augmented reality, computer vision, music and robotics. Looking to bring innovative new experiences to life, Chris has built installations for the Ontario Science Centre and contributed to major technology art projects featured around the world (including Burning Man).

Can you share with us how your love of Robotics and Brain-Machine Interfaces (BMI) began?

When I was very young, instead of playing with popular/trendy childrens toys, I was interested in tools so much so, that my favorite book was actually a catalogue of tools (at 18 months) and I wanted a sewing machine for Christmas when I was 3.

I was interested in what tools could do how they could extend my reach into the impossible, and my love for robotics and BMI was simply an extension of that. I was so curious about what lay just beyond the limits of my bodys capabilities, just beyond the range of my senses. It makes a lot of sense in a way, as I believe we humans love to figure things out whether its through our senses or through applying our knowledge and our tools together to explore and make sense of our experiences.

I didnt start building robots or BMIs until much later, Im pretty sure it was just a question of access. Computers werent so affordable (or approachable) in the 80s. I learned to program on a Commodore 64,but I didnt want my creations to only live in a computer. I learned to wire things into the parallel port, but it was frustrating and tedious. There was no Arduino, no raspberry pie, no next day deliveries from Digikey.

The coolest thing I built back then was a mask with some computer-controlled flashing lights that I could pulsate into my eyes at different frequencies. I had noticed that my perception got a little weird looking at flickering LEDs in my tinkering, so I was curious about what would happen if I affected my entire vision that way. Clearly I had a latent interest in consciousness and the brain-machine interface. Im really curious about what I might have built if I had access to Muse or other hackable technologies of today back then!

What were some of the first robots that you worked on?

I built a really cool wall-climbing robot with a couple of friends. It had four vacuum cups for hands and a big vacuum belly. The only use we could think of for it was autonomous window cleaning. It was a super fun project enabled by the kindness of automation vendors who gave us parts when we cold-called them with a crazy idea but it actually worked! The project also taught us a lot about electromagnetic interference and the strength of the drywall in the house.

Following that, I built a painting robot one summer that painted on a huge 68 wall canvas using a brush mounted to a mutant commodore 64 printer. It was a monstrosity that used every bit of tech junk I could find including a barbecue tank, computer mice and my old rollerblades. It had a webcam from the mid-90s and attempted to draw what it saw. It was so ridiculous I still miss its patient, humorous personality.

When I was doing my masters, I built a similarly whimsical robot with some friends that was the size of a house. We were interested in what would happen if a building changed shape and personality in response to the people who were in it. It was super cooland the building felt alive! It movedand made noise. You became so aware of yourself, it felt like being in an empty cathedral.

For over a decade you essentially became a cyborg. Can you share your story of how this journey began?

By the time I finished my undergraduate degree computers had become pretty capable. I could afford a computer that could do simple processing of video at 15 frames per second,Linux was almost installable by the uninitiated. I loved the memory and speed of computers and it lead me to ask: What if I had similar abilities?

I met this professor at UofT named Steve Mann who was a wild inventor, and still a member of the InteraXon advisory board today. He walked around with a computer on his head and sent laser images into his eyes. It was exactly what I was looking for! If you love tools, what better thing to do than encrust yourself with them?

Steve and I started working a lot together. We were both interested in extending our overall perception. We worked a lot with computer vision and built very early augmented reality devices. In many ways, they still amaze me more than the AR thats available today. Steve had invented a way of creating perfect optical alignment between computer graphics and your natural view of the world. This allowed us to do beautiful things like melding information from a far-infrared camera seamlessly into your vision. Walking around and being able to see heat is really interesting.

You scaled back your cyborg ambitions, as it caused you to distance yourself from others. Could you share some details about this transition in your mindset?

I had imagined a deep and seamless integration with computing technology: Information always available, instant communication, AI assistants, and extended-sensory abilities. I really believed in technology always being there so I could have it when needed.

Things changed for me when I started broadcasting images to a website. A local telecom company donated a bunch of mobile phones with serial data connections to our lab at the university. We could slowly upload images, about one every few seconds at low fidelity. We started a challenge to see who could stream the most. It was a super interesting experiment. I wore computers for months streaming my life to the internet, making sure to post every few seconds whenever I was doing something interesting living my life through a camera view.

The truth is, it was exciting to feel like I wasnt alone, posting to an imagined audience. Sound familiar? We all got a taste of present-day social media, 20 years ago. And what did I learn?

Being stuck in a computer, trying to connect with others by broadcasting a virtual life, kept me from being present with others and I found myself feeling more alone than ever. Woah.

I walked around with constant information overload with a computer terminal in front of my face signalling anytime an email came in, and when an image was uploaded a text web browser would open with something I was researching it was a lot.

Though I was interested in computers helping me solve problems, I began to experience less freedom of thought. I felt constantly interrupted, being triggered by what was bubbling up through cyberspace. I discovered the challenge of staying in touch with who you are and the loss of ability to tune into your spark of creativity when you are always in a state of information overload.

I was interested in technology that made me feel expansive, creative, and unfettered, but somehow, I painted myself into a corner with much of the opposite.

You did a really remarkable societal experiment, where users across Canada could use their minds to control lights on the CN Tower and Niagara Falls using their minds. Could you describe this?

This was a special opportunity we had early on in the journey of Muse at the winter Olympics in 2010, in an effort to connect the various parts of Canada to the global event.

While its not yet understood, we know that our brainwaves synchronize in interesting ways,especially when we do things in a close relationship, like communicate with each other, when we dance or when making music. What happens when you project the brain activity of an individual in a way for it to be experienced by many?

We created an experience where people attending the games on the west coast of Canada could affect the experience of thousands of people, 3000 miles away. By wearing a brain-sensing device, participants connected their consciousness to huge real-time lighting display that illuminated Niagara Falls, downtown Toronto via CN Tower, and the Canadian parliament buildings in Ottawa.

You sat in front of a huge screen with a real-time view of the light displays so you could see the live effect of your mind in this larger than life experience. People would call up friends in Toronto and get them to watch as the patterns of activity in their brain lit up the city with a dramatic play of light.

Youve described Muse as a happy accident. Could you share the details behind this happy accident, and what you learned from the experience?

I often forget the beauty of tinkering as building tech can be really tedious. You have to get rigid, but so much great stuff happens when you can break out the patch cables, plug a bunch of random stuff together and just see what happens just like how Muse was created!

The first seed of Muse was planted when we wrote some code to connect to an old medical EEG system and streamed the data over a network. We had to find a computer chassis that supported ISA cards and we made a makeshift headband. We wanted to get EEG data feeding into our wearable computers. Could we upload images automatically when we saw something interesting? We had heard that when you closed your eyes your alpha brainwaves would become larger could this be how we sense if we were interested in what we saw?

We hacked together some signal processing with some basic FFT spectral analysis and hooked up the result to a simple graphic that was like one of those vertical light dimmer sliders. Simple idea, but it was a pretty elaborate setup. What happened next was super interesting. We took turns wearing the device, closing and opening our eyes. Sure enough, the slider went up and down, but it would wander around in curious ways. When we closed our eyes it went up, but not all the way up and still wandered around What was happening?

We spent hours playing with it, trying to understand what made it wander and if we could we control it. We hooked the output to an audible sound so we could hear it go up and down when we had our eyes closed. I remember sitting there for ages, eyes closed, exploring my consciousness and the sound.

I soon discovered I could focus my consciousness in different ways, changing the sound, but also changing my experience, my perception and the way I felt. We invited other people into the lab and the same thing happened to them. They would close their eyes and go into a deep inner exploration (sounds kind of like meditation doesnt it?!). It was wild we completely forgot about our original idea as this was so much more interesting. That was the happy accident I can say I discovered meditation and mindfulness through technology, by accident!

Can you explain some of the technology that enables Muse to detect brainwaves?

The brain has billions of neurons, and each individual neuron connects (on average) to thousands of others. Communication happens between them through small electrical currents that travel along the neurons and throughout enormous networks of brain circuits. When all these neurons are activated they produce electrical pulses visualize a wave rippling through the crowd at a sports arena this synchronized electrical activity results in a brainwave.

When many neurons interact in this way at the same time, this activity is strong enough to be detected even outside the brain. By placing electrodes on the scalp, this activity can be amplified, analyzed, and visualized. This is electroencephalography, or EEG a fancy word that just means electric brain graph. (Encephalon, the brain, is derived from the ancient Greek enkphalos, meaning within the head.)

Muse has been tested and validated against EEG systems that are exponentially more expensive, and its used by neuroscientists around the world in real-world neuroscience research inside and outside the lab. Using 7 finely calibrated sensors 2 on the forehead, 2 behind the ears plus 3 reference sensors Muse is a next-generation, state of the art EEG system that uses advanced algorithms to train beginner and intermediate meditators at controlling their focus. It teaches users how to manipulate their brain states and how to change the characteristics of their brains.

The Muse algorithm technology is more complex than traditional neurofeedback. In creating the Muse app, we started from these brainwaves and then spent years doing intensive research on higher-order combinations of primary, secondary and tertiary characteristics of raw EEG data and how they interact with focused-attention meditation.

What are some of the noticeable meditative or mental improvements that you have personally noticed from using Muse?

My attention is more agile and its stronger. It sounds simple, but I know how to relax. I understand my emotions better and Im more in tune with others. Its truly life changing.

Outside of people that meditate, what other segments of the population are avid users of Muse?

There are a lot of biohackers and scientists some of which have done some really awesome things. Prof. Krigolson from UVic has been using Muse in the Mars habitat, and hes done experiments on Mount Everest with the monks who live in the monasteries on the mountain. There are also some awesome folks at the MIT media lab who are using Muse while sleeping to affect dreams. So cool.

Is there anything else that you would like to share about Muse?

Entering the world of sleep with our latest product release Muse S has been infinitely interesting from a product and research perspective, and very exciting when it comes to the positive applications Muse can have for so many people who are looking to get a better nights sleep.

Also, I personally love how Muse can render your brain activity as sound. From years of studying biosignals, something Ive never grown tired of is the beauty in these waves that flow within us. Like the waves of the ocean, they are infinitely complex, yet simple and familiar. I love that we are beautiful inside, and I love the challenge of bringing that out and celebrating it as sound and music.

Thank you for the great interview, I look forward to getting my hands on the Muse, anyone who wishes to learn more or to order a unit should visit the Muse website.

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Artificial Intelligence Used to Analyze Opinions Through Brain Activity - Unite.AI

inContext.ai Awarded $225K by the National Science Foundation to Develop Artificial Intelligence Tools for Healthcare – Business Wire

HOUSTON--(BUSINESS WIRE)--inContext.ai, a developer of healthcare-centric information extraction software and AI-powered clinical applications, announced an award of $225K in National Science Foundation (NSF) funding. The inContext.ai platform improves physicians interactions with complex and counter-intuitive software by augmenting and assisting the physicians workflow, decreasing the possibility of medical errors and compromised care while reducing the frustrations that lead to physician burnout.

80% of diagnostic information is in the form of free-text reports, rendering it difficult to access and make actionable. said Dr. Grzeszczuk, CEO of inContext.ai. Our goal is to make dark clinical data which is buried in diagnostic reports, accessible and actionable anytime, anywhere. The generous support from the NSF will enable us to work in collaboration with our partners at leading academic institutions to improve the accuracy of our existing tools and develop new ones.

NSF is proud to support the technology of the future by thinking beyond incremental developments and funding the most creative, impactful ideas across all markets and areas of science and engineering, said Andrea Belz, Division Director of the Division of Industrial Innovation and Partnerships at NSF. With the support of our research funds, any deep technology startup or small business can guide basic science into meaningful solutions that address tremendous needs.

inContext.ai is eager to fulfill Phase I requirements and is excited to be in contention for the $1M Phase II follow-on funding, said Dr. Grzeszczuk.

The proposal was submitted in collaboration with Lucille Packard Childrens Hospital at Stanford University School of Medicine (Palo Alto, CA) and Texas Childrens Hospital (Houston, TX).

About inContext.ai

Based in Houston TX, with offices in Los Angeles, CA and Vancouver, BC, inContext.ai develops cognitive technology and tools that streamline delivery of patient care and decrease medical errors by helping clinicians focus on what they do best: caring for patients. Implementation of inContext.ais Deep Artificial Intelligence (AI) tools supporting Natural Language Processing (NLP) tasks including Question Answering (QA), Reading Comprehension (RC), and Natural Language Inference (NLI), which seamlessly synchronize with EHR systems, means healthcare providers remain in compliance with medical/legal/regulatory responsibilities, significantly improve cost, quality, and access to care. For more information, please visit http://www.incontext.ai

About the National Science Foundation's Small Business Programs: Americas Seed Fund powered by NSF awards $200 million annually to startups and small businesses, transforming scientific discovery into products and services with commercial and societal impact. Startups working across almost all areas of science and technology can receive up to $1.75 million to support research and development (R&D), helping de-risk technology for commercial success. Americas Seed Fund is congressionally mandated through the Small Business Innovation Research (SBIR) program. The NSF is an independent federal agency with a budget of about $8.1 billion that supports fundamental research and education across all fields of science and engineering.

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inContext.ai Awarded $225K by the National Science Foundation to Develop Artificial Intelligence Tools for Healthcare - Business Wire

Artificial Intelligence in Agriculture Market Analysis Of Global Trends, Demand And Competition 2020-2028 – Cole of Duty

Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Artificial Intelligence in Agriculture market.

Trusted Business Insights presents an updated and Latest Study on Artificial Intelligence in Agriculture Market 2019-2026. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Artificial Intelligence in Agriculture market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

Get Sample Copy of this Report @ Artificial Intelligence in Agriculture Market Size, Market Research and Industry Forecast Report, 2025 (Includes Business Impact of COVID-19)

Industry Insights, Market Size, CAGR, High-Level Analysis: Artificial Intelligence in Agriculture Market

The global artificial intelligence in agriculture market size was valued at USD 608.9 million in 2018 and is anticipated to register a CAGR of 25.4% from 2019 to 2025. Artificial intelligence techniques for farming help increase productivity and yield. Therefore, agribusiness corporations adopt artificial intelligence technologies in terms of predictive analytics-based solutions. AI-based applications and techniques help control pests, yield healthier crops, monitor the soil, and improve agriculture-related tasks in the entire food supply chain. Artificial intelligence is increasingly being adopted in the agriculture industry for the improvement of harvest quality and accuracy since it helps analyze farm data.The global population is expected to reach 9.8 billion by 2050, according to the UN. Rapidly growing population drives the need for bringing AI in the agriculture industry. Limited arable land availability and need for increased food production for food security drive the need for a green revolution fueled by artificial intelligence, Internet of Things (IoT), and big data. AI-enabled applications cater to several areas in the agriculture industry, such as predictive and recommendation analytics, identifying plant diseases, detecting pest infestations, and soil monitoring.

Artificial intelligence solutions comprising robots, drones, and ground-based wireless sensors are increasingly being deployed in the agriculture industry. For instance, in November 2017, Microsoft collaborated with the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) to build an AI Sowing App. Furthermore, NatureFresh Farms, a U.S.-based tech company, is working on developing an AI technology to analyze plant information at scale to produce accurate harvest and yield forecasts. This AI technology predicts how long the blossom would take to ripen by using an artificial intelligence algorithm.Furthermore, automation in agriculture helps allocate resources such as fertilizers and water, determine the optimal date for crop sowing, and detect weeds, thereby driving the demand for artificial intelligence solutions. It also helps recommend how many seeds must be sowed by using historical long-term weather reports, production data, commodity pricing predictions, and seed information, among other inputs. The multiple benefits of artificial intelligence-based applications encourage several tech giants and start-ups to develop IoT-enabled devices for the deployment of AI applications for agriculture on a large scale.

Component Insights of Artificial Intelligence in Agriculture Market

The market is classified on the basis of component into hardware, software, and service. The software segment is expected to have a significant market share among components. Major players such as IBM, Microsoft, and Deere & Company offer AI-based solutions for the agriculture industry. AI-based software boost crop productivity and yield by implementing prediction-based analytics and computer vision.Moreover, increasing penetration of predictive analytics-based software boosts the growth of the software segment. Prominent predictive analytics-based software include Watson Decision Platform by IBM Corporation, AI Sowing App by Microsoft, and See and Spray pesticide and herbicide distribution systems by Deere & Company. These artificial intelligence solutions help farmers determine the optimal dates for crop sowing; detect crop diseases; monitor crop yield; and determine the required amount of land, fertilizers, water, and pesticides. Advantages of AI-enabled software for potential application areas, such as precision farming and drone analytics, further drives the growth of the software segment in the AI in agriculture market.

Application Insights of Artificial Intelligence in Agriculture Market

On the basis of application, the market has been classified into precision farming, drone analytics, agriculture robots, livestock monitoring, and others. The precision farming segment is expected to account for a significant market share over the projected period. Precision farming is one of the fastest-growing AI-enabled applications in agriculture. It helps farmers minimize costs and optimize resources effectively.Precision farming uses AI for data collection, interpretation, and analysis of digital data. For instance, GPS-equipped combine harvesters deploy artificial intelligence to track the harvest yield for field variability analysis, such as differences in water, soil makeup, or fungus, to produce georeferenced data. The analysis and predictions enable farmers to customize fertilizers or pesticides accordingly. Agriculture robots controlled by an AI system combine artificial intelligence, field sensors, and data analytics and can be effectively used for a variety of applications. These robots are efficient harvesting systems as they have the ability to weed and hoe. Increasing adoption of artificial intelligence in agriculture and new developments in robotics drive the agriculture robots segment.

Technology Insights of Artificial Intelligence in Agriculture Market

By way of technology, the market is segmented into machine learning and deep learning, predictive analytics, and computer vision. Several agribusiness corporations adopt predictive analytics to deploy artificial intelligence. For instance, AgEagle Aerial Systems Inc.; Microsoft; and Granular, Inc.; have worked on a prediction-based analytics technology to develop AI-enabled solutions and platforms for farming and agriculture.

The significant challenges faced by the agriculture industry are pesticide control, weed management, irrigation and drainage management, weather tracking, and crop disease infestations. Predictive analysis helps farmers analyze and address these challenges with the use of image analysis and neural networks. Furthermore, drone-enabled agricultural solutions have been introduced to support predictive analytics. For instance, AgEagle Aerial Systems Inc., focused on using artificial intelligence to enhance crop yield production, offers drone analytical solutions for the identification of concerned areas in crop fields and irrigation management. Since predictive analytics provides more efficiency in agricultural applications, the segment is expected to witness a steady CAGR over the forecast period. Moreover, by applying machine learning to sensor data, farm management systems are evolving into real artificial intelligence systems, increasing the scope of production improvement. Therefore, the machine learning and deep learning segment is also expected to witness growth.

Regional Insights of Artificial Intelligence in Agriculture Market

The market in North America accounted for a share of more than 35.0% in 2018, owing to the leading industrial automation industry and adoption of artificial intelligence solutions in the region. North America is characterized by improved purchasing power of the population, continuous investments in automation, considerable investments in IIoT, and increasing focus from governments on in-house AI equipment production. The market also benefits from the presence of numerous agricultural technology providers exploring artificial intelligence solutions, including IBM Corporation; Deere & Company; Microsoft; Granular, Inc.; and The Climate Corporation.The Asia Pacific market is expected to demonstrate the highest CAGR over the forecast period. Its growth is attributed to increasing adoption of artificial intelligence technologies in agriculture. Emerging economies such as India and China are leveraging the adoption of artificial intelligence solutions such as remote monitoring technology and predictive analysis in the food industry. Furthermore, the rising demand to create smart cities in these economies is encouraging agribusiness companies to adopt AI-leveraging solutions and services.

Market Share Insights of Artificial Intelligence in Agriculture Market

Key industry participants in the market include IBM Corporation; Microsoft; Deere & Company; AgEagle Aerial Systems Inc.; The Climate Corporation; Granular, Inc.; Descartes Labs, Inc.; Prospera Technologies; Taranis; aWhere Inc.; GAMAYA; ec2ce; PrecisionHawk; VineView; and Tule Technologies Inc.Vendors providing artificial intelligence solutions for agriculture focus on increasing their customer base to gain a competitive edge in the market by adopting several strategic initiatives such as collaborations, acquisitions, mergers, and partnerships. For instance, in May 2019, Deere & Company partnered with Cultivating New Frontiers in Agriculture (CNFA), an international agricultural development organization to increase productivity and income for smallholder farmers by implementing mechanization in the agriculture industry. In October 2018, The Climate Corporation collaborated with three agriculture-tech companies, SoilOptix; AgCon Aerial Corp.; and A&L Canada Laboratories Inc., to deliver new capabilities for farmers and expand its digital agriculture platform, Climate FieldView.

Segmentations, Sub Segmentations, CAGR, & High-Level Analysis overview of Artificial Intelligence in Agriculture Market Research ReportThis report provides forecasts for revenue growth at the global, regional, and country levels and analyses of the latest industry trends in each of the sub-segments from 2014 to 2025. For the purpose of this study, this market research report has segmented the global artificial intelligence in agriculture market report based on component, technology, application, and region:

Component Outlook (Revenue, USD Million, 2019 2030)

Hardware

Software

Service

Technology Outlook (Revenue, USD Million, 2019 2030)

Machine Learning & Deep Learning

Predictive Analytics

Computer Vision

Application Outlook (Revenue, USD Million, 2019 2030)

Precision Farming

Drone Analytics

Agriculture Robots

Livestock Monitoring

Others

Quick Read Table of Contents of this Report @ Artificial Intelligence in Agriculture Market Size, Market Research and Industry Forecast Report, 2025 (Includes Business Impact of COVID-19)

Trusted Business InsightsShelly ArnoldMedia & Marketing ExecutiveEmail Me For Any ClarificationsConnect on LinkedInClick to follow Trusted Business Insights LinkedIn for Market Data and Updates.US: +1 646 568 9797UK: +44 330 808 0580

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Artificial Intelligence in Agriculture Market Analysis Of Global Trends, Demand And Competition 2020-2028 - Cole of Duty

How Artificial Intelligence is Transforming the Online Casino Industry – Wales247

The concept of artificial intelligence (AI) is certainly nothing new. In fact, this term was first defined by the Oxford Dictionary as far back as 1955.

At the time, artificial intelligence was nothing more than a theoretical concept that was being examined within laboratory settings. This was largely due to the fact that computing power lagged far behind human imagination. However, we are now living within a world that has become rife with these advanced digital systems. The online casino industry is beginning to capitalise upon such advancements and this brings up an important question. What can players expect in the near future and how will the presence of AI transform their overall experiences? Let us first look at why artificial intelligence is being employed by virtual casinos before highlighting what the future may have in store.

Why AI and Why Now?

Online casinos fully appreciate the fact that they are facing stiff competition from similar digital providers. As geographic borders will not often come into play, it is important for developers to market to as large of an audience as possible. This is when the role of AI can serve to massively benefit their operations. Artificial intelligence offers several distinct advantages such as:

Players will remain engaged for longer periods of time and as a result, even novices can quickly become loyal customers. This is obviously beneficial for online casinos that hope to enjoy higher retention rates. However, users will also be able to leverage the variety of gaming options at their disposal.

A Bright Digital Future

It is a well-known fact that many professional casino reviews focus upon features such as rewards programmes, VIP packages, the variety of available games, and which software developers are associated with a specific website. This is quite important, as well-known providers such as Microgaming and NetEnt are one step ahead of the curve in regards to the technology that they are deploying. Artificial intelligence is only one example of what is now being incorporated into what they have to offer. This is also why players who are looking to enjoy a truly immersive experience should opt for larger online portals.

We also need to appreciate that artificial intelligence is becoming even more advanced on a monthly basis. While these systems will never be able to replace organic human interactions, they are undoubtedly set to transform the entire online casino sector. This is great news for anyone who does not have the time to travel to a brick-and-mortar establishment. Of course, the wide selection of available games will also prove beneficial in terms of variety. The bottom line is that AI is set to make waves throughout the digital landscape and we should expect to read even more about this innovative technology in the coming years.

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How Artificial Intelligence is Transforming the Online Casino Industry - Wales247

Rings that can tell you if you’re infected, artificial intelligence that can predict the next outbreak: does coronavirus detection tech actually work?…

This week L.A. Mayor Eric Garcetti made a very big deal about new thermal imaging cameras installed at LAX that will detect if travelers are running fevers, a big indication that a person might be infected with COVID-19. You'll probably be seeing those cameras showing up in many more places beyond just the airport.

The cameras are just the start: how about wearing a $300 ring that is supposed to warn you several days in advance of getting sick that you're likely infected with COVID........the NBA is offering these rings to all of its players returning to finish their season. There's also artificial intelligence algorithms that will predict where the next COVID outbreak will happen.

Bryan Walsh is the Future Correspondent for Axios covering emerging technology & trends.

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Rings that can tell you if you're infected, artificial intelligence that can predict the next outbreak: does coronavirus detection tech actually work?...

Advantages of Artificial Intelligence | Top 7 Most Useful …

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Advantages of Artificial Intelligence | Top 7 Most Useful ...

Artificial intelligence, machine learning, deep learning …

Artificial intelligence (AI) brings with it a promise of genuine human-to-machine interaction. When machines become intelligent, they can understand requests, connect data points and draw conclusions. They can reason, observe and plan. Consider:

Clearly, were not talking about robotic butlers. This isnt a Hollywood movie. But we are at a new level of cognition in the artificial intelligence field that has grown to be truly useful in our lives.

We get it, though. Youre still confused about how all these topics AI, machine learning and deep learning relate. Youre not alone. And we want to help.

In this article well explore the basic components of artificial intelligence and describe how various technologies have combined to help machines become more intelligent.

So where did AI come from? Well, it didnt leap from single-player chess games straight into self-driving cars. The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning.

Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.

For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google, Amazon or Microsoft tackled similar projects.

This work paved the way for the automation and formal reasoning that we see in computers today.

As a whole, artificial intelligence contains many subfields, including:

While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly."

Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems.

With AI, you can ask a machine questions out loud and get answers about sales, inventory, customer retention, fraud detection and much more. The computer can also discover information that you never thought to ask. It will offer a narrative summary of your data and suggest other ways to analyze it. It will also share information related to previous questions from you or anyone else who asked similar questions. Youll get the answers on a screen or just conversationally.

How will this play out in the real world? In health care, treatment effectiveness can be more quickly determined. In retail, add-on items can be more quickly suggested. In finance, fraud can be prevented instead of just detected. And so much more.

In each of these examples, the machine understands what information is needed, looks at relationships between all the variables, formulates an answer and automatically communicates it to you with options for follow-up queries.

We have decades of artificial intelligence research to thank for where we are today. And we have decades of intelligent human-to-machine interactions to come.

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Artificial intelligence, machine learning, deep learning ...

Deloitte Launches the Deloitte AI Institute to Advance Applied Artificial Intelligence Innovation and Research for the Enterprise – PRNewswire

NEW YORK, June 24, 2020 /PRNewswire/ -- Deloitte today announced the launch of the Deloitte AI Institute, a center that focuses on artificial intelligence (AI) research, eminence and applied innovation across industries. The Institute will bring together the brightest minds in the field of AI to apply cutting-edge research to help address a wide spectrum of relevant AI use cases.

"The Deloitte AI Institute is being established to advance the conversation and development of AI for enterprises," said Nitin Mittal, AI co-leader and principal, Deloitte Consulting LLP. "Our goal is to blend Deloitte's deep experience in applied AI with a robust network of some of the most intelligent AI minds in the world to challenge the status quo. Through the power of this center, we aim to deliver impactful and game-changing research; and innovation to help our clients lead in the 'Age of With,' a world where humans work side-by-side with machines."

The Institute's network will consist of top industry thought leaders and academic luminaries; start-ups; research and development groups; entrepreneurs; investors; and innovators. This network of specialists and research, combined with Deloitte's depth of applied AI knowledge and understanding of pain points across industries and sectors whether it is identifyinguse cases, understanding industry specific ecosystems, scaling from AI proof-of-concepts or securing AI systems can help organizations transform quickly with AI.

"With our unique experience, investments in AI and work with top organizations, we believe the Deloitte AI Institute can ignite ground-breaking applied AI solutions for enterprises," saidBeena Ammanath, executive director of Deloitte AI Institute, Deloitte Consulting LLP. "Further, to help enterprises advance with AI, we will aim to help organizations remain distinctively human in a technology-driven world."

In addition, as enterprises continue to navigate the complexity of AI ethics, the Deloitte AI Institute will collaborate with leading ethicists, thought leaders and organizations to raise awareness and provide services.

"In today's world, the benefits of AI enable greater outcomes for organizations than ever before. However, ethical safeguards must be put into place to help protect reputation and future performance," said Irfan Saif, Deloitte Risk & Financial Advisory principal, Deloitte & Touche LLP and Deloitte AI co-leader. "With AI ethics, the Institute aims to help organizations achieve a positive future by bringing together top stakeholders from all sectors of society to discuss and co-design effective policies and frameworks, such as Deloitte's Trustworthy AI framework, for governing AI."

To learn more about the Deloitte AI Institute and its catalogue of research and articles, such as the bi-annual State of AI in the Enterprise study and Trustworthy AI framework, visit our website.

About DeloitteDeloitte provides industry-leading audit, consulting, tax and advisory services to many of the world's most admired brands, including nearly 90% of the Fortune 500 and more than 7,000 private companies.Our people work across the industry sectors that drive and shape today's marketplace delivering measurable and lasting results that help reinforce public trust in our capital markets, inspire clients to see challenges as opportunities to transform and thrive, and help lead the way toward a stronger economy and a healthy society. Deloitte is proud to be part of the largest global professional services network serving our clients in the markets that are most important to them.Now celebrating 175 years of service, our network of member firms spans more than 150 countries and territories. Learn how Deloitte's more than 312,000 people worldwidemake an impact that matters atwww.deloitte.com.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as "Deloitte Global") does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the "Deloitte" name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see http://www.deloitte.com/aboutto learn more about our global network of member firms.

SOURCE Deloitte Consulting LLP

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Deloitte Launches the Deloitte AI Institute to Advance Applied Artificial Intelligence Innovation and Research for the Enterprise - PRNewswire

Bringing the predictive power of artificial intelligence to health care – MIT News

An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital before they to return to the doctors office with further complications.

But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that arent optimized for their patients.

Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.

The platform is being used to help hospitals determine which patients are most likely to miss appointments, acquire infections like sepsis, benefit from periodic check ups, and more. Health insurers, in turn, are using ClosedLoop to make population-level predictions around things like patient readmissions and the onset or progression of chronic diseases.

We built a health care data science platform that can take in whatever data an organization has, quickly build models that are specific to [their patients], and deploy those models, says ClosedLoop co-founder and Chief Technology Officer Dave DeCaprio 94. Being able to take somebodys data the way it lives in their system and convert that into a model that can be readily used is still a problem that requires a lot of [health care] domain knowledge, and thats a lot of what we bring to the table.

In light of the Covid-19 pandemic, ClosedLoop has also created a model that helps organizations identify the most vulnerable people in their region and prepare for patient surges. The open source tool, called the C-19 Index, has been used to connect high-risk patients with local resources and helped health care systems create risk scores for tens of millions of people overall.

The index is just the latest way that ClosedLoop is accelerating the health care industrys adoption of AI to improve patient health, a goal DeCaprio has worked toward for the better part of his career.

Designing a strategy

After working as a software engineer for several private companies through the internet boom of the early 2000s, DeCaprio was looking to make a career change when he came across a project focused on genome annotation at the Broad Institute of MIT and Harvard.

The project was DeCaprios first professional exposure to the power of artificial intelligence. It blossomed into a six year stint at the Broad, after which he continued exploring the intersection of big data and health care.

After a year in health care, I realized it was going to be really hard to do anything else, DeCaprio says. Im not going to be able to get excited about selling ads on the internet or anything like that. Once you start dealing with human health, that other stuff just feels insignificant.

In the course of his work, DeCaprio began noticing problems with the ways machine learning and other statistical techniques were making their way into health care, notably in the fact that predictive models were being applied without regard for hospitals patient populations.

Someone would say, I know how to predict diabetes or I know how to predict readmissions, and theyd sell a model, DeCaprio says. I knew that wasnt going to work, because the reason readmissions happen in a low-income population of New York City is very different from the reason readmissions happen in a retirement community in Florida. The important thing wasnt to build one magic model but to build a system that can quickly take somebodys data and train a model thats specific for their problems.

With that approach in mind, DeCaprio joined forces with former co-worker and serial entrepreneur Andrew Eye, and started ClosedLoop in 2017. The startups first project involved creating models that predicted patient health outcomes for the Medical Home Network (MHN), a not-for-profit hospital collaboration focused on improving care for Medicaid recipients in Chicago.

As the founders created their modeling platform, they had to address many of the most common obstacles that have slowed health cares adoption of AI solutions.

Often the first problems startups run into is making their algorithms work with each health care systems data. Hospitals vary in the type of data they collect on patients and the way they store that information in their system. Hospitals even store the same types of data in vastly different ways.

DeCaprio credits his teams knowledge of the health care space with helping them craft a solution that allows customers to upload raw data sets into ClosedLoops platform and create things like patient risk scores with a few clicks.

Another limitation of AI in health care has been the difficulty of understanding how models get to results. With ClosedLoops models, users can see the biggest factors contributing to each prediction, giving them more confidence in each output.

Overall, to become ingrained in customers operations, the founders knew their analytics platform needed to give simple, actionable insights. That has translated into a system that generates lists, risk scores, and rankings that care managers can use when deciding which interventions are most urgent for which patients.

When someone walks into the hospital, its already too late [to avoid costly treatments] in many cases, DeCaprio says. Most of your best opportunities to lower the cost of care come by keeping them out of the hospital in the first place.

Customers like health insurers also use ClosedLoops platform to predict broader trends in disease risk, emergency room over-utilization, and fraud.

Stepping up for Covid-19

In March, ClosedLoop began exploring ways its platform could help hospitals prepare for and respond to Covid-19. The efforts culminated in a company hackathon over the weekend of March 16. By Monday, ClosedLoop had an open source model on GitHub that assigned Covid-19 risk scores to Medicare patients. By that Friday, it had been used to make predictions on more than 2 million patients.

Today, the model works with all patients, not just those on Medicare, and it has been used to assess the vulnerability of communities around the country. Care organizations have used the model to project patient surges and help individuals at the highest risk understand what they can do to prevent infection.

Some of it is just reaching out to people who are socially isolated to see if theres something they can do, DeCaprio says. Someone who is 85 years old and shut in may not know theres a community based organization that will deliver them groceries.

For DeCaprio, bringing the predictive power of AI to health care has been a rewarding, if humbling, experience.

The magnitude of the problems are so large that no matter what impact you have, you dont feel like youve moved the needle enough, he says. At the same time, every time an organization says, This is the primary tool our care managers have been using to figure out who to reach out to, it feels great.

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Bringing the predictive power of artificial intelligence to health care - MIT News

Artificial intelligence is where our natural talents lie – The Australian Financial Review

BAE Systems Australia is also providing the unmanned flight vehicle management component of Boeing Australias Loyal Wingman Advanced Development Program that is being developed with the RAAF and DST Group.

In a three-decade R&D program, BAE Systems Australia worked with the University of Sydneys Australian Centre for Field Robotics during the 1990s to develop robotics and autonomous control technologies, says Mr Yelland.

It explored GPS-free navigation and decentralised data fusion technologies for swarms of unmanned vehicles, much of which was subsequently adopted by mining giant Rio Tinto for use on remote mine sites in Australia.

Rheinmetall Mission Master operating autonomously as a battlefield ambulance carrying two casualties.

Now, as the industry lead for land autonomy in Defences new, Brisbane-based Trusted Autonomous Systems CRC, BAE Systems Australia is working with the Australian Army and DSTs Land Division to develop a technology road map to tackle this most challenging of operational environments.

Last year it successfully demonstrated autonomously controlled M113 armoured personnel carriers.

Rheinmetall Defence Australia isnt part of this CRC but its German parent has been researching autonomous vehicles for two decades.

The Australian Armys new Boxer 8x8 combat reconnaissance vehicles will be manufactured at the companys new Military Vehicle Centre of Excellence (MILVEHCOE) at Redbank, Queensland.

Having the hardware moving safely alongside and amongst an infantry combat team as a member of the team, that is one of the key differentiators were working on here.

Gary Stewart, Rheinmetall Defence Australia MD

There, Rheinmetall Defence Australia engineers are leading 60 researchers across Australia in a two-year, $12 million robotics and autonomy R&D program in partnership with DST, CSIROs Data61, QUT and RMIT as well as with German and Canadian researchers.

When we stood up the R&D program we wanted to make sure we had the best and brightest, Gary Stewart, managing director of Rheinmetall Defence Australia, told The Australian Financial Review.

RMIT is one of the worlds leading video game development hubs and so an expert in machine learning; QUT has world-leading expertise in artificial vision how robots interpret the world around them; and CSIROs Data61 is a global leader in Artificial Intelligence.

The company already has two Canadian wheeled Mission Master robotic vehicles in-country and will receive a lightweight German Wiesel Wingman tracked armoured vehicle later this year.

The aim, says Mr Stewart, is to start demonstrating genuinely transformational autonomous vehicle technologies to the Australian Army in 2020, COVID-19 permitting.

The vehicle wont be controlled by a member of the infantry team its supporting it will be completely autonomous.

The Nulka missile decoy system at work.

Having the hardware moving safely alongside and amongst an infantry combat team as a member of the team, that is one of the key differentiators were working on here, he said.

This culminates in the vehicle driving and making its own decisions, interpreting the infantry hand signals, watching how the soldiers move and then determining the appropriate way it should behave as a member of that team.

Rheinmetall Defence Australias R&D is all company funded, says Mr Stewart. But we are looking for co-investment opportunities.

For defence giant Lockheed Martin Australia, R&D is its lifeblood, according to Dr Tony Lindsay, Director of the companys Melbourne-based STELaRLab.

The company is now investing locally in a portfolio of advanced technologies, including quantum science, hypersonics, space systems and AI.

It is also the Combat Systems Integrator (CSI) for the Navys new Attack-class submarines and this month announced eight contracts worth a combined $600,000 with the Universities of Adelaide, South Australia, Tasmania and Melbourne and three local high-technology firms to prepare white papers on the development of novel and emerging combat system technologies.

This follows $900,000-worth of similar contracts awarded last year and funded under its submarine contract.

Lockheed Martin is working with the white paper authors on longer-term research based on these. Topics include underwater communications, dynamic computing resource allocation, and AI-enabled novel operational concepts associated with the use of uninhabited and autonomous systems by a submarine.

The main focus of Lockheed Martin Australias Australian industrial participation program is R&D and it has funded some 32 export-focused R&D projects since 2012, the company says.

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Artificial intelligence is where our natural talents lie - The Australian Financial Review