Daily Archives: January 26, 2020

The Pentagon launched its Joint Artificial Intelligence Center in 2018 to strategically unify and accelerate AI applications across the nations defense and military enterprise. Insiders at the center have now spent about nine months executing that defense driven AI-support.

At an ACT-IAC forum in Washington Wednesday, Rachael Martin, the JAICs mission chief of Intelligent Business Automation Augmentation and Analytics, highlighted insiders early approach to automation and innovation.

Our mission is to transform the [Defense] business process through AI technologies, to improve efficiency and accuracybut really to do all those things so that we can improve our overall warfighter support, Martin said.

Within her specific mission area, Martin and the team explore and develop automated applications that support a range of efforts across the Pentagon, such as business administration, human capital management, acquisitions, finance and budget training, and beyond. Because the enterprise is vast, the center is selective in determining the projects and programs best fit to be taken under its wing.

For the JAIC, there are a couple of key principles that we want to go by, or that we're going to adhere to when we're looking at a project and whether we support it, Martin explained.

The first principle to be evaluated is mission impact. In this review, insiders pose questions like who cares? she said. They assess the user-base that would most benefit from the project and what the ultimate outcome would be across Defense if the JAIC opted to support it. Next, according to Martin, officials review data-readiness. In this light, insiders address factors like where the data to be used is storedand whether its actually prepped for AI, or more advanced analysis and modeling to run on top of it.

The third factor thats assessed is technology maturity. Martin said that contrary to what many seem to think, the JAIC is not a research organization but instead seeks to apply and accelerate their adoption of already-existing solutions across the department and where those improvements are needed most. Insiders are therefore not at all interested in spending heaps of time researching new, emerging AI and automation applications. Instead, they aim to identify what already exists and is ready to be deployed at this moment.

So that's a big one for us that we like to emphasize, Martin said.

The final assessment is whether the JAIC can identify Defense insiders who will actually use whatever they are set to build. When developing something new, Martin said insiders want to those itll eventually touch to weigh in on the development every step of the way.

We're not in the business of coming up with good ideas and then creating something and trying to hoist it on somebody else, Martin said. We really believe in a very user-centric approach.

Read this article:

How the Pentagon's JAIC Picks Its Artificial Intelligence-Driven Projects - Nextgov

Sergey Tarasov - stock.adobe.com

Last Friday, the World Economic Forum (WEF) sent out a press announcement about an artificial intelligence (AI) toolkit for corporate boards. The release pointed to a section of their web site titled Empowering AI Leadership. For some reason, at this writing, there is no obvious link to the toolkit, but the press team was quick to provide the link. It is well laid out in linked we pages, and some well-produced pdfs are available for download. For purposes of this article, I have only looked at the overview and the ethics section, so here are my initial impressions.

As would be expected from an organization focused on a select few in the world, the AI toolkit is high level. Boards of directors have broad but shallow oversight over companies, so there is no need to focus on details. Still, it is wished that a bit more accuracy had been involved.

The description of AI is very nice. There are many definitions and, as Ive repeatedly pointed out, the meaning of AI and of machine learning (ML) continue to both be changing and to have different meanings to many. The problem in the setup is one that many people miss about ML. In the introductory module, the WEF claims The breakthrough came in recent years, when computer scientists adopted a practical way to build systems that can learn. They support that with a link to an article that gets it wrong. The breakthrough mentioned in the article, the level of accuracy in an ML system, is far more driven by a non-AI breakthrough than a specific ML model.

When we studied AI in the 1980s, deep learning was known and models existed. What we couldnt do is run them. Hardware and operating systems didnt support the needed algorithms and the data volumes that were required to train them. Cloud computing is the real AI breakthrough. The ability to link multiple processors and computers in an efficient and larger virtual machine is what has powered the last decades growth of AI.

I was also amused about with list of core AI techniques where deep learning and neural networks are listed at the same level as the learning methods used to train them. Im only amused, not upset, because boards dont need to know the difference to start, but its important to introduce them to the terms. I did glance at the glossary, and its a very nice set of high-level definitions of some of these so interested board members can get some clarification.

On a quick tangent, their definition of bias is well done, as only a few short sentences reference both the real world issue of bias and the danger of bias within an AI system.

Ethics are an important component (in theory) to the management of companies. The WEF points out at the beginning of that module that technology companies, professional associations, government agencies, NGOs and academic groups have already developed many AI codes of ethics and professional conduct. The statement reminds me of the saying that standards are so important that everyone wants one of their own. The module then goes on to discuss a few of the issue of the different standards.

Where I differ from the WEF should be no surprise. This section strongly minimized governmental regulation. Its all up to the brave and ethical company. As Zuckerbergs decision that Facebook will allow lies in political advertisements as long as it makes the firm and himself wealthier, it is clear that governments must be more active in setting guidelines on technical companies, both in at large and within the AI arena. Two years ago, I discussed how the FDA is looking at how to handle machine learning. Governments move slowly, but they move. Its clear that companies need to be more aware of the changing regulatory environment. Ethical companies should be involved in both helping governments set reasonable regulations, ones that protect consumers as well as companies, and should be preparing systems, in advance, to match where they think a proper regulatory environment will evolve.

The WEFs Davos meetings are, regardless of my own personal cynicism about them, where government and business leaders meet to discuss critical economic issues. Its great to see the WEF taking a strong look at AI and then presenting what looks like a very good, introductory, toolkit for boards of directors, but the need for strong ethical positions means that more is needed. It will be interesting to see how their positioning advances over the next couple of years. F

Read more from the original source:

The World Economic Forum Jumps On the Artificial Intelligence Bandwagon - Forbes

ORLANDO -- Though golf has a tendency to move slower than most industries, the technology innovations we've seen this week beg to differ. Artificial intelligence and robotics have been terms perhaps thrown around in the past, implemented by only the biggest companies, but now we're actually seeing the results of intense research and development. And that's especially true in the golf instruction realm, where lessons can have so much added value with the right set of data and smart products.

There were too many items to say this is a definitive list. But this is at least what caught our eye at the 2020 PGA Merchandise Show in the ever-expanding tech/instruction space.

1 . Hack Motion wrist sensor. Teachers (and students) want the ability to capture swing data and analyze it immediately. The biofeedback from Hack Motion, a wrist-motion training system, is synced to your tablet or smartphone (or computer). It tracks your wrist movement in real time, measuring wrist flexion, extension and rotation via a device you wear like a watch, in addition to a Velcroed strap you wear around your forefingers. After a quick minute calibration, the system captures any swing you make and delivers the data to the app, where you can study it. There's also access to tour-player data the company has captured over the past two-plus years. A Latvian-based company, CEO Atis Hermanas says the company has sold units to more than 40 countries over the past two years. It had leading coaches David Orr, James Leitz, Brian Manzella and others speaking to the benefits of the technology at the PGA Show. With audio feedback and seven hours of battery life, there's a lot to like about Hack Motion.

2 . TrackMan's new A.I. technology (Tracy). TrackMan's continued iterations on its existing technology will be fun to follow. It will continue to innovate on its launch-monitor technology to expand its offerings, including its simulator business. Perhaps most impressive is its new Tracy technology, which TrackMan unveiled at the PGA Show, with a soft June 1 launch. Tracy, adapted from tracer, is a mode you can turn on and off, which will recommend what you should work on based on a minimum of six shots on a TrackMan device. It will audibly communicate with you (if you want it to), with voice commands that ask game-analysis questions, and it will make recommendations based on the (estimated) 500-million-plus shots the company has collected around the world. As the company says, it pinpoints what you need to work, not how to work on itencouraging you to seek the advice of an instructor.

3 . V1 + BodyTrak partnership. Applying ground force in the golf swing with proper sequencing is one of the hot instruction applications these days, as we continue to study how tour pros apply such force to their tee shots. A partnership between BodiTrak, which measures vertical force and velocity through a portable pressure matand V1 Sports, one of the first to penetrate the instruction/app spacehopes to deliver a complete way to capture data and study the kinematic sequencing. The package goes for $3,500.

RELATED: PGA Show 2020: Five affordable new launch monitors geared for the everyman

4 . K-Motion's Smart Tiles. You've likely seen or heard about K-Motion's K-Vest motion-capture technology, which allows 3-D swing data to be captured and analyzed. With the system's new Smart Tiles, debuting at the PGA Show, a player's wrist and body movements are immediately captured and stored in the system's cloud-based improvement panel, allowing your teacher to provide feedback immediately. Color-coded cues also make it easy to understand which area of your swing you need the most help, along with auditory feedback to specify the positions you need to get into. (K-Player, the individual, consumer version of the technology goes for $2,495. And K-Coach, which includes the Smart Tiles, is $5,495.)

RELATED: Our best golf instruction: Jack Nicklaus' best tips

5. Dragonfly. A new player in this space, Guided Knowledgea British based companyhas introduced a smart suit with 18 sensors that a golfer can wear underneath their golf clothes and take it on the course. 3-D data is captured in real time as you play your round, and it's viewable via a remote coaching app with hundreds of performance metrics. Instead of being tied to the range or a pro's teaching facility, you can play your round and have sensors, from head to toe, measure your movements for improvement. "Players no longer have to be in a lab or a teacher's facility," says Jon Dalzell, chief science officer of Guided Knowledge. "What used to be an appointment is now available anywhere, anytime."

RELATED: You can now rent (or own) your own robotic putting simulator unit (if you have the money)

6 . Uneekor Eye XO launch monitor. The incredible explosion of simulators and launch monitors within golf has been fun to follow. The new Eye XO from Uneekor is a little different. It's an overhead launch monitor with non-marking ball technology, which will work outsidea convenience for some. With two cameras, down the line and face-on capturing 200 frames per second, in addition to a stereographic lens overhead, the system is able to provide a very crisp, sharp video of the golfer making contact with the ball, with video from each angle showing the ball traveling off the face, without any pixelation. Doug Bybee, with experience over 25 years within the industry as a fitter with Mizuno and Cobra among others, says he whiteboarded the concept for the company's new product just two days before his team in South Korea developed a software and cloud solution. He unveiled the prototype at the PGA Show, with the product marked for a June 1 launch ($10-12,000 for the complete program, or $1,250 for the pair of straight cameras.

RELATED: A GPS and a speaker all in one: Bushnell unveiled its innovative product this week

7 . U.S. Kids teaching app. One of the leaders in youth golf, U.S. Kids unveiled a new app this week that will allow instructors to organize and track kids' progress to deliver feedback to the player and coach. The digital Player Pathway scoreboard includes color-coded levels for each player, too, for easier sorting and tracking. Just like the other items above, it's making life easier on the teacher, so they can be more efficient with their time, and help more players ... a win for all.

RELATED: Golf instruction truths: The one move you need to make better iron contact

WATCH: GOLF DIGEST VIDEOS

Continue reading here:

7 new ways golf instruction is embracing artificial intelligence and innovative technology - Golf Digest

Neon, the artificial human prototype conceptualized by computer scientist and inventor Pranav Mistry, created waves recently. The President and CEO of Samsung's STAR Labs told ET in an exclusive interview that he created Neon because human beings are unable to connect with artificial intelligence (AI) assistants such as Apples Siri.

The Palanpur (Gujarat)-born Mistry, considered one of the best innovative minds in the world right now, said Neon will be a companion to the elderly and to those who are lonely and could even work as fashion models or news anchors. The 38-year-old also spoke about the dangers posed by AI,echoing Google parent Alphabet Incs chief Sundar Pichai who recently called upon governments to regulate AI. Edited Excerpts:

When you started thinking about Neon, what was the problem you were trying to solve?

Two years ago, when we started thinking about it (we wanted to) push the boundaries of science so that can the machine interfaces become just like humans. It solves a lot of problems and opens up a lot of opportunities that never existed before. A lot of businesses are coming to us (for Neon), from media companies, film and even the fashion industry.

Say, for example, if ICICI Bank has domain-specific knowledge, (and) they need to talk to customers in a particular way (then) think about a powerful front-end interface that can connect to any third-party services for B2B applications. When it comes to consumer applications, in the long run, we need to build something that doesn't exist someone who can have the human aspects, emotions...

As a science, this thing never existed. Tomorrow, it might solve healthcare problems, or education problems or the loneliness problem in the United Kingdom.

Initially, what could Neon be used for?

I was talking to the chairman of a big media company in (South) Korea. They say the major problem is breaking news at night since you need to wake up the whole crew. We're not here to replace the main news anchor, but how about (if) your favourite news anchor is on your phone and tells you hey, you missed yesterday's game. This is what happened. That personal connection is more important.

As a kid, I grew up with Mickey Mouse. These cartoon characters are not just in our movies, they are in our dreams. My daughter is sad when Princess Fiona is sad; the human connection is what keeps us attached as a human. And, that is the reason behind doing this.

So, it's like a face to AI assistants like Alexa or Siri?

Yes, AI doesnt have a face right now.No technology has that interface. Even when our soldiers are in the field, somebody needs to talk to them. Are they mentally healthy? You can put a questionnaire to them --On a scale of 0-6 how are you feeling?. Thats not the right interface in that situation. If someone could only to talk to them...

An old person...has an AI system to give him all the answers at his home, but what he is looking for is someone to talk to. Because, that's where the loneliness problem is coming in. If you notice all these problems of loneliness or autism...they are coming from countries that already have everything -- every single piece of technology -- they can watch any movie, but they still feel lonely... because current technology is not solving that problem. Because there is no human face to the technology... and that is why I am doing this. Star Labs is an independent future factory. No one decides what Star Labs does. It is fully backed by Samsung, but Samsung does not ask a single question.

Theoretical physicist and cosmologist, the late Stephen Hawking, had said that AI could be dangerous. Others, too, have echoed those thoughts. Are you concerned about what you are building?

Every technology, if you take even the smallest neon bulb right now, can be misused.Nuclear fusion is a threat to the world right now, but this is the same nuclear fusion that drives more than 50% of the worlds electricity. AI has its own problems, but why I feel even more comfortable about this is because if I don't do this thing right now, in two years time someone else will be talking about it...

I feel comfortable because...I understand what the ethical standards of this thing should be, because I am imagining and dreaming a better world, not just a richer world for a few. So many of my products, ideas like Sixth Sense were open source; when I did Galaxy Gear, it was nothing to do with money.

What are the checks and balances an AI ecosystem needs to have?

This phase of AI the understanding of it in the scientific world, technology world as well as the consumer world is very different. Core people who are actually behind this know where this technologys expertise ends. They know that AI has nothing to do with intelligence.

AI is a smart database system. If you do not show a machine 1,000 pictures of an elephant, it cannot recognise it. It doesnt even know what an elephant is. The limitations of the current approaches to AI end there, because nature doesnt work this way. My daughter can see a vague drawing and tell you that its an elephant, but an AI machine will not do the same because the approach is not the same.

But, we still need the tabs, both as a consumer community as well as the scientific and technology community. We need to put the boundaries of architecture on the design level, like science fiction movies, like in any robotics movie there used to be the three rules of robotics, such as robots will not hurt humans etc. Why this is important is because anything has potential to explode or to make the world better.

So, who puts in those rules?Thats what the world needs to decide -- the core thinkers of the world. And thats why I feel, if I dont do it (build Neon), someone else will do it. Thats why I feel much safer that what is going to go will go past me. I would love to have that conversation, not at the government level but at the community level that, can we ensure there are rules and everybody follows them.

See more here:

Human beings are unable to connect with artificial intelligence: Pranav Mistry - ETtech.com

According to some, artificial intelligence (AI) is thenew electricity. Like electricity, AI will transform every major industry and open new opportunities that were never possible. However, unlike electricity, the ethics surrounding the development and use of AI remain controversial, which is a significant element constraining AIs full potential.

The Defense Innovation Board (DIB) released a paper in October 2019 that recommends the ethical use of AI within the Defense Department. It described five principles of ethically used AI responsible, equitable, traceable, reliable, and governable. The paper also identifies measures the Joint Artificial Intelligence Center, Defense Agency Research Projects Agency (DARPA), and U.S. military branches are taking to study the ethical, moral, and legal implications of employing AI. While the paper primarily focused on the ethics surrounding the implementation and use of AI, it also argued that AI must have the ability to detect and avoid unintended harm. This article seeks to expand on that idea by exploring AIs ability to operate within the Defense Department using an ethical framework.

Designing an ethical framework a set of principles that guide ethical choice for AI, while difficult, offers a significant upside for the U.S. military. It can strengthen the militarys shared moral system, enhance ethical considerations, and increase the speed of decision-making in a manner that provides decision superiority over adversaries.

AI Is Limited without an Ethical Framework

Technology is increasing the complexity and speed of war. AI, the use of computers to perform tasks normally requiring human intelligence, can be a means of speeding decision-making. Yet, due to a fear of machines inability to consider ethics in decisions, organizations are limiting AIs scope to focus ondata-supported decision-making using AI to summarize data while keeping human judgment as the central processor. For example, leaders within the automotive industry received backlash for programming self-driving cars to make ethical judgments. Some professional driving organizations have demanded that these cars be banned from the roads for at least 50 years.

This backlash, while understandable, misses the substantial upside that AI can offer to ethical decision-making. AI reflectshuman inputand operates on human-designed algorithms that set parameters for the collection and correlation of data to facilitate machine learning. As a result, it is possible to build an ethical framework that reflects a decision-makers values. Of course, when the data that humans supply is biased, for example, AI can mimic its trainers bydiscriminating on gender and race. Biased algorithms, to be sure, are a drawback. However, bias can be mitigated by techniques such as counterfactual fairness, Google AIs recommended practices, and algorithms such as those provided by IBMs AI Fairness 360 toolkit. Moreover, AI processing power makes it essential for successfully navigating ethical dilemmas in a military setting, where complexity and time pressure often obscure underlying ethical tensions.

A significant obstacle to building an ethical framework for AI is a fundamental element of war the trade-off between human lives and other military objectives. While international humanitarian law provides a codification of actions, many of which have ethical implications, it does not answer all questions related to combat. It primarily focuses on defining combatants, the treatment of combatants and non-combatants, and acceptable weapons. International humanitarian law does not deal with questions concerning how many civilian deaths are acceptable for killing a high-valued target, or how many friendly lives are worth sacrificing to take control of a piece of territory. While, under international law, these are examples of military judgments, this remains an ethical decision for the military leader responsible.

Building ethical frameworks into AI will help the military comply with international humanitarian law and leverage new opportunities while predicting and preventing costly mistakes in four ways.

Four Military Benefits of an Ethical AI Framework

Designing an ethical framework for AI will benefit the military by forcing its leaders to reexamine existing ethical frameworks. In order to supply the benchmark data on which AI can learn, leaders will need to define, label, and score choice options in ethical dilemmas. In doing so they will have three primary theoretical frameworks to leverage for guidance: consequentialist, deontological, and virtue. While consequentialist ethical theories focus on the consequences of the decision (e.g., expected lives saved), deontological ethical theories are concerned with the compliance with a system of rules (refusing to lie based on personal beliefs and values despite the possible outcomes). Virtue ethical theories are concerned with instilling the right amount of a virtuous quality into a person (too little courage is cowardice; too much is rashness; the right amount is courage). A common issue cited as anobstacle to machine ethicsis the lack of agreement on which theory or combination of theories to follow leaders will have to overcome this obstacle. This introspection will help them better understand their ethical framework, clarify and strengthen the militarys shared moral system, andenhance human agency.

Second, AI can recommend decisions that consistently reflect a leaders preferred ethical decision-making process. Even in high-stakes situations, human decision-making is prone to influence from factors that have little or nothing to do with the underlying choice. Things like poor nutrition, fatigue, and stress all common in warfare can lead to biased and inconsistent decision-making. Other influences, such as acting in ones self-interest or extreme emotional responses, can also contribute tomilitary members making unethical decisions. AI, of course, does not become fatigued or emotional. The consistency of AI allows it to act as a moral adviser by providing decision-makers morally relevant data leaders can rely on as their judgment becomes impaired. Overall, this can increase the confidence of young decision-makers, a concern thecommander of U.S. Army Training and Doctrine Commandbrought up early last year.

Third, AI can help ensure that U.S. military leaders make the right ethical choice however they define that in high-pressure situations. Overwhelming the adversary is central to modern warfare. Simultaneous attacks anddeception operationsaim to confuse decision-makers to the point where they can no longer use good judgment. AI can process and correlate massive amounts of data to provide not only response options, but also probabilities that a given option will result in an ethically acceptable outcome. Collecting battlefield data, processing the information, and making an ethical decision is very difficult for humans in a wartime environment. Although the task would still be extremely difficult, AI can gather and process information more efficiently than humans. This would be valuable for the military. For example, AI that is receiving and correlating information from sensors across the entire operating area could estimate non-combatant casualties, the proportionality of an attack, or social reactions from observing populations.

Finally, AI can also extend the time allowed to make ethical decisions in warfare. For example, a central concern in modern military fire support is the ability to outrange the opponent, to be able to shoot without being shot. The race to extend the range of weapons to outpace adversaries continues to increase the time between launch and impact. Future warfare will see weapons that are launched and enter an area that is so heavily degraded and contested that the weapon will lose external communication with the decision-maker who chose to fire it. Nevertheless, as the weapon moves closer to the target, it could gain situational awareness on the target area and identify changes pertinent to the ethics of striking a target. If equipped with onboard AI operating with an ethical framework, the weapon could continuously collect, correlate, and assess the situation throughout its flight to meet the parameters of its programmed framework. If the weapon identified a change in civilian presence or other information altering the legitimacy of a target, the weapon could divert to a secondary target, locate a safe area to self-detonate, or deactivate its fuse. This concept could apply to any semi- or fully autonomous air, ground, maritime, or space assets. The U.S. military could not afford a weapon system deactivating or returning to base in future conflicts each time it loses communication with a human. If an AI-enabled weapon loses the ability to receive human input, for whatever reason, an ethical framework will allow the mission to continue in a manner that aligns the weapons actions with the intent of the operator.

Conclusion

Building an ethical framework for AI will help clarify and strengthen the militarys shared moral system. It will allow AI to act as a moral adviser and provide feedback as the judgment of decision-makers becomes impaired. Similarly, an ethical framework for AI will maximize the utility of its processing power to help ensure ethical decisions when human cognition is overwhelmed. Lastly, providing AI an ethical framework can extend the time available to make ethical decisions. Of course, AI is only as good as the data it is provided.

AI should not replace U.S. military leaders as ethical decision-makers. Instead, if correctly designed, AI should clarify and amplify the ethical frameworks that U.S. military leaders already bring to war. It should help leaders grapple with their own moral frameworks, and help bring those frameworks to bear by processing more data than any decision-maker could, in places where no decision-maker could go.

AI may create new programming challenges for the military, but not new ethical challenges. Grappling with the ethical implications of AI will help leaders better understand moral tradeoffs inherent in combat. This will unleash the full potential of AI, and allow it to increase the speed of U.S. decision-making to a rate that outpaces its adversaries.

Ray Reeves is a captain in the U.S. Air Force and a tactical air control party officer and joint terminal attack controller (JTAC) instructor and evaluator at the 13thAir Support Operations Squadron on Fort Carson, Colorado. He has multiple combat deployments and is a doctoral student at Indiana Wesleyan University, where he studies organizational leadership. The views expressed here are his alone and do not necessarily reflect those of the U.S. government or any part thereof. Linkedin.

Image: U.S. Marine Corps (Photo by Lance Cpl. Nathaniel Q. Hamilton)

More:

The Ethical Upside to Artificial Intelligence - War on the Rocks

PUNE, India, Jan. 23, 2020 /PRNewswire/ -- The global artificial intelligent (AI) in retail market size is estimated to reach USD 23,426.3 million by 2026, exhibiting a CAGR of 33.7% during the forecast period. The ongoing shift by retailers from traditional retail experience to AI-driven business solutions is one of the crucial factors enabling the growth of the AI in retail market. According to an article published by Forbes, 83% believe AI is a strategic priority for their businesses today and 84% of respondents say AI will enable them to obtain or sustain a competitive advantage. The growing awareness about the advantages of AI in retail operations such as quality improvement, paced up decision making, strong operational agility and enhanced customer experience will boost the AI in retail market growth in the forthcoming years. In addition, exceptional benefits of AI-powered data analytics will further spur demand for AI in retail market in the foreseeable future.

According to the report, published by Fortune Business Insights in a report, titled "Artificial Intelligence (AI) in Retail MarketSize, Share & Industry Analysis, By Offering (Solutions, Services), By Function (Operations-Focused, Customer-Facing), By Technology (Computer Vision, Machine Learning, Natural Language Processing, and Others), and Regional Forecast, 2019-2026" the AI in retail market size was valued at USD 2,306.8 million in 2018. The report is aimed at delivering a comprehensive view of the AI in retail market dynamics, structure by identifying and providing information regarding the key market segments. It also focuses on an all-encompassing analysis of leading market players by financial position, product, product portfolio, price, growth strategies, and regional presence. It offers porter's analysis and SWOT analysis to record the question of shareholders and highlights the investment potential in the upcoming future. It also showcases different procedures and strategies of companies currently operating in the market. It further examines the components convincing market expansion, growth patterns, restricting factors and market strategies.

To gain more insights into the market with detailed table of content and figures, click here: https://www.fortunebusinessinsights.com/artificial-intelligence-ai-in-retail-market-101968

Disposition to AI-powered Chat Bots by Retailers Will Encourage Market Expansion

The growing inclination of retail brands for the deployment of AI-powered chatbots for customer engagement will augment healthy growth of the market in the forthcoming years. Retail brands are able to handle several queries simultaneously with the help of chatbots, without the need to employ a large workforce. The positive impact of artificial intelligence on customer relations and sales will spur demand for AI in retail. Furthermore, close-ended chatbots are configured to answer shopping-related questions, provide quick support and suggestions besides offering a better resolution to their problem. Customers are more likely to engage with AI-powered chatbots, which, in turns enhances customer loyalty. These factors combined will accelerate the AI in retail market share in the foreseeable future.

Emergence of Virtual Trial Rooms to Bolster Healthy Growth

The increasing popularity of virtual trial rooms and ongoing development across retail supply chains will aid the AI in retail market trend during the forecast period. For instance, virtual trial rooms enable buyers to try different dresses without actually having to wear them with the help of digital mirrors. Further, AI allows shoppers to experiment with their outfit by means of a touch-based interface. In addition, the launch of virtual rooms by major companies to create growth opportunities for the market. For instance, Fitiquette, a fashion Web site uses a ground-breaking technology that allows customers to try on outfits in a virtual 3D world based on the user's exact body dimensions. The trial room gives a 360-degree view of the fit and drape of a garment on the actual body dimensions of the consumer.

Request a Sample Copy: https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/artificial-intelligence-ai-in-retail-market-101968

Introduction of AI Integrated Products and Solutions to Foster Growth in Asia Pacific

North America was valued at USD 1,102.0 million and is expected to remain dominant during the forecast period. The rising deployment of AI-based solutions by retailers to improve the supply chain operations and product portfolio will contribute positively to the AI in retail market revenue. Asia Pacific is expected to grow rapidly during the forecast period owing to the launch of various AI integrated products and solutions. For instance, China is expected to be a world-leading AI center by 2030.

List of the Major Companies in the Global Artificial Intelligence (AI) in Retail Market include:

Have Any Query? Ask Our Experts:https://www.fortunebusinessinsights.com/enquiry/speak-to-analyst/artificial-intelligence-ai-in-retail-market-101968

Table of Content:

o Definition, By Segment

o Research Approach

o Sources

o Drivers, Restraints and Opportunities

o Emerging Trends

o Macro and Micro Economic Indicators

o Consolidated SWOT Analysis of Key Players

o Porter's Five Forces Analysis

o Key Findings / Summary

o Market Size Estimates and Forecasts

Continued..!!!

Request for Customization:https://www.fortunebusinessinsights.com/enquiry/customization/artificial-intelligence-ai-in-retail-market-101968

(Have a Look at Reports Trending inInformation & TechnologyIndustry)

Browse Related Reports:

Internet of Things (IoT) MarketSize, Share and Industry Analysis By Platform (Device Management, Application Management, Network Management), By Software & Services (Software Solution, Services), By End-Use Industry (BFSI, Retail, Governments, Healthcare, Others) And Regional Forecast, 2019 2026

Artificial Intelligence (AI) MarketSize, Share and Global Trend by Component (Hardware, Software, Services), By Technology (Computer Vision, Machine Learning, Natural Language Processing, Others), By Industry Vertical (BFSI, Healthcare, Manufacturing, Retail, IT & Telecom, Government, Others) and Geography Forecast till 2026

Big Data Technology Market Size, Share & Industry Analysis, By Offering (Solution, Services), By Deployment (On-Premise, Cloud, Hybrid), By Application (Customer Analytics, Operational Analytics, Fraud Detection and Compliance, Enterprise Data Warehouse Optimization, Others), By End Use Industry (BFSI, Retail, Manufacturing, IT and Telecom, Government, Healthcare, Utility, Others) and Regional Forecast, 2019-2026

Retail Cloud MarketSize, Share & Industry Analysis, By Model Type (Infrastructure as a Service, Platform as a Service and Software as a Service), By Deployment (Public, Private and Hybrid Cloud), By Solution (Supply Chain Management, Workforce Management, Customer Management, Reporting & Analytics, Data Security, Omni-Channel), By Enterprise Size (Small & Medium and Large Enterprise) and Regional Forecast, 2019-2026

Natural Language Processing (NLP) MarketSize, Share & Industry Analysis, By Deployment (On-Premises, Cloud, and Hybrid), By Technology (Interactive Voice Response (IVR), Text Analytics, Speech Analytics, Pattern and Image Recognition, and Others), By Industry Vertical (Healthcare, Retail, BFSI, Automotive & Transportation, Advertising & Media, Manufacturing, and Others) and Regional Forecast, 2019-2026

Unified Communication as a Service (UCaaS) MarketSize, Share & Industry Analysis, By Component (Telephony, Unified Messaging, Collaboration Platforms, Conferencing, and Reporting and Analytics), By Organization Size (SMEs, Large Enterprises), By End-User (Banking, Financial Services, and Insurance (BFSI), IT and Telecommunications, Healthcare, Public Sector and Utilities, and Others) and Regional Forecast, 2019-2026

3D Printing MarketSize, Share & Industry Analysis, By Component (Hardware, Software, and Services), By Technology (Fused Deposition Modeling, Selective Laser Sintering, Stereolithography, Direct Metal Laser Sintering, PolyJet, Multi Jet Fusion (MJF), Others) By Applications (Prototyping, Production, Proof of Concept, and Others), By End-Use (Automotive, Aerospace and Defense, Healthcare, Others) and Regional Forecast, 2019-2026

Internet of Things (IoT) in Manufacturing MarketSize, Share & Industry Analysis, By Platform (Device Management, Application Management, Network Management), By Software & Services (Software Solution and Services), By Application (Predictive Maintenance, Asset Tracking and Management, Logistics and Supply Chain Management, Real-Time Workforce Tracking and Management, Emergency and Incident Management and Others) and Regional Forecast, 2019-2026

Cyber Security MarketSize, Share & Industry Analysis, By Component (Solutions, Services), By Deployment (On-Premises, Cloud), By Organization Size (SMEs, Large Enterprises), By End-User (BFSI, IT and Telecommunications, Retail, Healthcare, Government, Manufacturing, Travel and Transportation, Energy and Utilities and Others) and Regional Forecast, 2019 2026

Virtual Reality MarketSize, Share & Industry Analysis, By Offering (Hardware, Software), By Technology (Nonimmersive, Semi-Immersive), By Industry Vertical (Gaming & Entertainment Media, Healthcare, Education, Automotive, Aerospace & Defense, Manufacturing), By Application (Training & Simulation, Educational, Attraction, Research & Development) and Regional Forecast, 2019 2026

Endpoint Security MarketSize, Share and Global Trend By Component (Software, Services), By Deployment (On-Premise, Cloud), By Enterprise Size (Large Enterprises, Small & Medium Enterprises), By End-Use Industry (BFSI, Telecom & IT, Retail, Healthcare, Government & Public Sector, Transportation, and Others) and Geography Forecast, 2019-2026

Fraud Detection and Prevention MarketSize, Share and Industry Analysis By Component (Solutions, Services), By Application area (Insurance Claims, Services, Money Laundering), By Deployment (Cloud & On-premise), By Organization Size (Large, Small & Medium Enterprises), By Vertical (BFSI, IT and Telecommunication, Government,Travel and Transportation, Manufacturing, Healthcare and Life Sciences) and Geography Forecast, 2019 2026

Identity And Access Management MarketSize, Share and Industry Analysis By Component (Provisioning, Directory Services, Single Sign-On, Others), By Deployment Model (Cloud, On-Premises), By Enterprise Size (Large Enterprises, Small and Medium Enterprises), By Industry Vertical (BFSI, IT and Telecom, Retail and Consumer Packed Goods, Others) And Regional Forecast 2019-2026

About us:

Fortune Business Insights offers expert corporate analysis and accurate data, helping organizations of all sizes make timely decisions. We tailor innovative solutions for our clients, assisting them address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in.

Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.

At Fortune Business Insights we aim at highlighting the most lucrative growth opportunities for our clients. We therefore offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.

Contact Us:

Fortune Business Insights Pvt. Ltd. 308, Supreme Headquarters, Survey No. 36, Baner, Pune-Bangalore Highway, Pune - 411045, Maharashtra, India.Phone:US: +1-424-253-0390 UK: +44-2071-939123 APAC: +91-744-740-1245 Email: sales@fortunebusinessinsights.com Fortune Business Insights

LinkedIn| Twitter| Blogs

Press Release-https://www.fortunebusinessinsights.com/press-release/artificial-intelligence-ai-in-retail-market-9527

SOURCE Fortune Business Insights

Link:

Artificial Intelligence (AI) in Retail Market to Reach USD 23,426.3 Million by 2026; Rising Awareness About the Advantages of AI in Retail Operations...

Climate-driven change in the Gulf of Maine is raising new threats that "red tides" will become more frequent and prolonged. But at the same time, powerful new data collection techniques and artificial intelligence are providing more precise ways to predict where and when toxic algae will bloom. One of those new machine learning prediction models has been developed by a former intern at Bigelow Labs in East Boothbay.

In a busy shed on a Portland wharf, workers for Bangs Island Mussels sort and clean shellfish hauled from Casco Bay that morning. Wholesaler George Parr has come to pay a visit.

"I wholesale to restaurants around town, and if there's a lot of mackerel or scallops, I'll ship into Massachusetts," he says.

Butbusiness grinds to a halt, he says, when blooms of toxic algae suddenly emerge in the bay causing the dreaded red tide.

Toxins can build in filter feeders to levels that would cause "Paralytic Shellfish Poisoning" in human consumers. State regulators shut down shellfish harvests long before danger grows acute. But when a red tide swept into Casco Bay last summer, Bangs Island's harvest was shut down for a full 11 weeks.

So when the restaurants can't get Bangs Island they're like 'Why can't we get Bangs Island?' It was really bad this summer. And nobody was happy."

As Parr notes, businesses of any kind hate unpredictability. And being able to forecast the onset or departure of a red tide has been a challenge although that's changing with the help of a type of artificial intelligence called machine learning.

"We're coming up with forecasts on a weekly basis for each site. For me that's really exciting. That's what machine learning is bringing to the table," says Izzi Grasso, a recent Southern Maine Community College student who is now seeking a mathematics degree at Clarkson University.

Last summer Grasso interned at the Bigelow Laboratory for Ocean Sciences in East Boothbay. That's where she helped to lead a successful project to use cutting-edge "neural network" technology that is modeled on the human brain to better predict toxic algal blooms in the Gulf of Maine.

"Really high accuracy. Right around 95 percent or higher, depending on the way you split it up," she says.

Here's how the project worked: the researchers accessed a massive amount of data on toxic algal blooms from the state Department of Marine Resources. The data sets detailed the emergence and retreat of varied toxins in shellfish samples from up and down the coast over a three-year period.

The researchers trained the neural network to learn from those thousands of data points. Then it created its own algorithms to describe the complex phenomena that can lead up to a red tide.

Then we tested how it would actually predict on unknown data, says Grasso.

Grasso says they fed in data from early 2017 which the network had never seen and asked it to forecast when and where the toxins would emerge.

"I wasn't surprised that it worked, but I was surprised how well it worked, the level of accuracy and the resolution on specific sites and specific weeks," says Nick Record, Bigelow's big data specialist.

Record says that the network's accuracy, particularly in the week before a bloom emerges, could be a game-changer for the shellfish industry and its regulators.

Once it's ready, that is.

"Basically it works so well that I need to break it as many ways as I can before I really trust it."

Still, the work has already been published in a peer-reviewed journal, and it is getting attention from the scientific community. Don Anderson is a senior scientist at the Woods Hole Oceanographic Institution who is working to expand the scope of data-gathering efforts in the Gulf.

"The world is changing with respect to the threat of algal blooms in the Gulf of Maine," he says. "We used to worry about only one toxic species and human poisoning syndrome. Now we have at least three."

Anderson notes, though, that machine-learning networks are only as good as the data that is fed into them. The Bigelow network, for instance, might not be able to account for singular oceanographic events that are short and sudden or that haven't been captured in previous data-sets such as a surge of toxic cells that his instruments detected off Cutler last summer.

"With an instrument moored in the water there, and we in fact got that information, called up the state of Maine and said you've got to be careful, there's a lot of cells moving down there, and they actually had a meeting, they implemented a provisional closure just on the basis of that information, which was ultimately confirmed with toxicity once they measured it," says Anderson.

Anderson says that novel modeling techniques such as Bigelow's, coupled with an expanded number of high-tech monitoring stations, like Woods Hole is pioneering in the Gulf, could make forecasting toxic blooms as simple as checking the weather report.

"That situational awareness is what everyone's striving to produce in the field of monitoring and management of these toxic algal blooms, and it's going to take a variety of tools, and this type of artificial intelligence is a valuable part of that arsenal." Back at the Portland wharf, shellfish dealer George Parr says the research sounds pretty promising.

"Forewarned is fore-armed, Parr says. If they can figure out how to neutralize the red tide, that'd be even better."

Bigelow scientists and former intern Izzi Grasso are working now to look "under the hood" of the neural network, to figure out how, exactly, it arrives at its conclusions. They say that could provide clues about how not only to predict toxic algal blooms,but even how to prevent them.

This story is a production of New England News Collaborative. A version of this story was originallypublishedby Maine Public Radio.

Read this article:

Artificial Intelligence Could Help Scientists Predict Where And When Toxic Algae Will Bloom - WBUR

Learning by doing is a great thing unless its costing you money, then it may not seem worth it. In the business world when new technologies come around it may be tempting to take a wait and see approach to them, watching your competitors successes and failures before taking the time to implement new technologies. Unfortunately when it comes to artificial intelligence (AI), the potential payoff is too big to ignore, and waiting to see how your competitors do implementing this technology could leave your business in the dust.

Taking the right steps toward implementing AI is crucial. Some companies know that they need to hire a data scientist but they dont know what they expect the person to do and they will try to hire someone with no framework or plan in place.

The first step toward integrating artificial intelligence into your business strategy is to take it seriously and make a plan for how it will work. Start with an end goal in mind and work your way back from there. Next, figure out exactly what AI application will help you achieve that goal. With that in mind, start a pilot program with a targeted goal, keeping in mind that it could take a year or more to see any results from such a program.

While going through the growing pains of implementing an enterprise-wide system of AI, it may seem as though this technology is a huge waste of time and money. Learning by doing is a valuable way to understand the ins and outs of any new technology, and even if your companys experiment fails you can still gain valuable insights from failures. These insights can help you find a better focus for your next artificial intelligence experiment.

Currently, fewer than a third of AI pilots progress past the exploratory state to be fully implemented. This does not mean failure, though, it simply means the hypothesis needs to be adjusted before the experiment is altered and started again from the beginning. As with any new technology, there are going to be growing pains and opportunities to learn more.

This can translate to many different outcomes. AI algorithms can be used to reduce waste and improve quality by removing unnecessary variables. It can help meet rising demand and cut variable costs through analysis. It can make adjustments and predict when the market will shift. Some examples from real businesses:

Today, more than 60% of business leaders urgently need to find and implement a strategy for using artificial intelligence in their businesses, but less than half of those actually have a plan. Learn more about implementing AI in your business below.

More here:

Implementing Artificial Intelligence In Your Business (infographic) - Digital Information World

Artificial Intelligence in Healthcare Application Market Size, Status and Growth Outlook 2019 To 2027

Report Description

A recent market intelligence report that is published by Data Insights Partner onthe global Artificial Intelligence in Healthcare Application marketmakes an offering of in-depth analysis of segments and sub-segments in the regional and internationalArtificial Intelligence in Healthcare Application market. The research also emphasizes on the impact of restraints, drivers, and macro indicators on the regional and globalArtificial Intelligence in Healthcare Application market overthe short as well as long period of time.

A detailed presentation of forecast, trends, and dollar values of globalArtificial Intelligence in Healthcare Application market isoffered. In accordance with the report, theglobal Artificial Intelligence in Healthcare Application market isprojected to expand at a CAGR of35%over the period of forecast.

Market Insight, Drivers, Restraints& Opportunity of the Market:

One of the greatest challenges in the healthcare arena is huge healthcare data and it is impossible to handle all of these data for medical personnel. Here comes the opportunity of artificial intelligence (AI) in healthcare field.

In simple words, AI is an area where computer or computer controlled machines can work like human beings. It increases the capability of healthcare practitioners to understand the daily patterns and requirements of people they are treating, and provide better service, guidance and supports for staying healthy.

The global Artificial Intelligence in Healthcare Application market is primarily driven by the growing demand of large and complex data set, emerging preference of healthcare cost cutting, and requirements of healthcare services between healthcare providers and the patients. For instance, IBM Watson is assisting healthcare organization by applying cognitive technology to unearth huge health data and power diagnosis.

In addition, this product is able to scrutinize and stock medical information, resources, case studies, medical discussions etc which is comparatively faster than any medical practitioner. Conversely, the use of AI in healthcare system in developing countries such as Latin America and Middle East and Africa is still not prominent due to the less awareness of AI in healthcare sector.

In addition, risk of data hacking, stringent regulatory standards across several verticals may restrain the growth of the global Artificial Intelligence in Healthcare Application market in coming future. However, new product developments, and strategic collaborations among key players may provide the global Artificial Intelligence in Healthcare Application market an opportunity to propel during the forecast period.

Request for Report Sample:datainsightspartner.com/requestle?ref=131

Segment Covered:

This market intelligence report on the global Artificial Intelligence in Healthcare Application market encompasses market segments based on deployment, technology, application, end user and region. On the basis of deployment, the sub-market is segregated into on-premise and cloud services.

In terms of technology, the market is classified into machine learning, natural language processing, context-aware computing, and computer vision. By application, the global market is categorized into patient data and risk analysis, hospital management, medical imaging, drug discovery, patient monitoring, and others.

In terms of end-user, the global market is classified into healthcare service providers, healthcare payors, patients, healthcare companies and others. By Geography, the global Artificial Intelligence in Healthcare Application market has been divided into North America (the U.S., Canada), Latin America (Brazil, Mexico, Argentina and other countries), Europe (Germany, France, the U.K., Spain, Italy, Russia, and other countries), Asia Pacific (India, Japan, China, Australia and New Zealand and other countries), Middle East and Africa (GCC, South Africa, Israel and Other countries).

Profiling of Market Players:

This business intelligence report offers profiling of reputed companies that are operating in the market. Companies such as IBM, Google, Microsoft, GE, Siemens, Medtronic, Amazon.com, Inc, Nvidia and others have been profiled into detail so as to offer a glimpse of the market leaders.

Moreover, parameters such as Artificial Intelligence in Healthcare Application related investment & spending and developments by major players of the market are tracked in this global report.

Report Highlights:

In-depth analysis of the micro and macro indicators, market trends, and forecasts of demand is offered by this business intelligence report. Furthermore, the report offers a vivid picture of the factors that are steering and restraining the growth of this market across all geographical segments.

In addition to that, IGR-Growth Matrix analysis is also provided in the report so as to share insight of the investment areas that new or existing market players can take into consideration. Various analytical tools such as DRO analysis, Porters five forces analysis has been used in this report to present a clear picture of the market.

The study focuses on the present market trends and provides market forecast from the year 2017-2027. Emerging trends that would shape the market demand in the years to come have been highlighted in this report.

A competitive analysis in each of the geographical segments gives an insight into market share of the global players.

Request for Report Analysis:datainsightspartner.com/report/market/131

Salient Features:

This study offers comprehensive yet detailed analysis of theArtificial Intelligence in Healthcare Application market, size of the market (US$ Mn), and Compound Annual Growth Rate (CAGR (%)) for the period of forecast: 2019 2027, taking into account 2018 as the base year

It explains upcoming revenue opportunities across various market segments and attractive matrix of investment proposition for the said market

This market intelligence report also offers pivotal insights about various market opportunities, restraints, drivers, launch of new products, competitive market strategies of leading market players, emerging market trends, and regional outlook

Profiling of key market players in the worldArtificial Intelligence in Healthcare Application marketis done by taking into account various parameters such as company strategies, distribution strategies, product portfolio, financial performance, key developments, geographical presence, and company overview

Leading market players covered this report comprise names suchIBM, Google, Microsoft, GE, Siemens, Medtronic, Amazon.com, Inc, Nvidia, andamong others

The data of this report would allow management authorities and marketers of companies alike to take informed decision when it comes to launch of products, government initiatives, marketing tactics and expansion, and technical up gradation

The world market forArtificial Intelligence in Healthcare Application catersto the needs of various stakeholders pertaining to this industry, namely suppliers, manufacturers, investors, and distributors forArtificial Intelligence in Healthcare Application market.The research also caters to the rising needs of consulting and research firms, financial analysts, and new market entrants

Research methodologies that have been adopted for the purpose of this study have been clearly elaborated so as to facilitate better understanding of the reports

Reports have been made based on the guidelines as mandated by General Data Protection Regulation

Ample number of examples and case studies have been taken into consideration before coming to a conclusion

Reasons to access:

vIdentify opportunities and plan strategies by having a strong understanding of the investment opportunities in theArtificial Intelligence in Healthcare Application market

vIdentification of key factors driving investment opportunities in theArtificial Intelligence in Healthcare Application market

vFacilitate decision-making based on strong historic and forecast data

vPosition yourself to gain the maximum advantage of the industrys growth potential

vDevelop strategies based on the latest regulatory events

vIdentify key partners and business development avenues

vRespond to your competitors business structure, strategy and prospects

vIdentify key strengths and weaknesses of important market participants

This email address is being protected from spambots. You need JavaScript enabled to view it.

Read this article:

Current research: Artificial Intelligence in Healthcare Application Market size, status and growth outlook 2027 - WhaTech Technology and Markets News

- Advertisement -

Abel Mocln, an archaeologist at the Centro Nacional de Investigacin sobre la Evolucin Humana (CENIEH), has led a study which combines Archaeology and Artificial Intelligence, published in the journalArchaeological and Anthropological Sciences, about the Navalmallo Rock Shelter site, situated in the locality of Pinilla de Valle in Madrid, which shows the activity by Neanderthal groups of breaking the bones of medium-sized animals such as deer, for subsequent consumption of the marrow within.

The particular feature of the study lies in its tremendous statistical potential. For the first time, Artificial Intelligence has been used to determine the agent responsible for breaking the bones at an archaeological site, with highly reliable results, which it will be possible to compare with other sites and experiments in the future.

Credit: CENIEH

We have managed to show that statistical tools based on Artificial Intelligence can be applied to studying the breaking of the fossil remains of animals which appear at sites, states Mocln.

In the work, it is not just this activity carried out by the Neanderthals which is emphasized, but also aspects of the methodology developed by the authors of the study. On this point, Mocln insists on the importance of Artificial Intelligence as this is undoubtedly the perfect line of work for the immediate future of Archaeology in general and Taphonomy in particular.

The largest Neanderthal settlement

The Navalmallo Rock Shelter, about 76,000 years old, offers one of the few large windows into Neanderthal behavior within the Iberian Meseta. With its area of over 300 m2, it may well be the largest Neanderthal camp known in the center of the Iberian Peninsula, and it has been possible to reveal different activities conducted by these hominins here, such as hunting large animals, the manufacture of stone tools and the systematic use of fire.

In this study, part of the Valle de los Neandertales project, which includes other locations in the archaeological site complex of Calvero de la Higuera, the collaborating researchers were Rosa Huguet, of the IPHES in Tarragona, Beln Mrquez and Csar Laplana, of the Museo Arqueolgico Regional in Madrid, as well as the three codirectors of the Pinilla del Valle project: Juan Luis Arsuaga, Enrique Baquedano and Alfredo Prez Gonzlez.

CENIEH

Header Image Abrigode Navalmallo Credit: CENIEH

- Advertisement -

Continued here:

Artificial intelligence to study the behavior of Neanderthals - HeritageDaily