Category Archives: Artificial Intelligence
The Surprising Way Artificial Intelligence Is Transforming Transportation – Forbes
Automotive technology concept.
How are data and AI transforming transportation?originally appeared onQuora:the place to gain and share knowledge, empowering people to learn from others and better understand the world.
AnswerbyJonathan Matus, CEO and Founder,Zendrive, onQuora:
While our growing dependencies on mobile phones stand to threaten road safety and increase rates of distracted driving, other technology innovations can work in safetys favor. Developments in 5G networks, autonomous vehicles, and artificial intelligence are poised to transform the way we drive and the safety of our roads.
5G will have a positive impact on road maintenance with faster data collection creating new possibilities around automation. Today, road crews have to physically go on-site to inspect a problem and determine what next steps are required. But through new video and sensor data, road maintenance crews will receive alerts of life-threatening hazards faster than ever. Connected vehicles equipped with dash cams will generate crowdsourced footage of potential debris and other hazards so that crews can act fast to alert drivers in the area and find safe solutions. Sensors on smartphones can produce similar insights already and offer insights in the interim. In addition to this, departments will be able to rank the urgency of various jobs by analyzing data from each location.
According to a report,94% of vehicle accidentsin the US involve human error and are potentially avoidable. With autonomous vehicle technology especially, theres the potential to essentially eliminate human error from the risk equation, decreasing the number of collisions and improving overall road safety. To achieve full autonomy, the onboard computers on self-driving cars need to make use of cameras and radar sensors to generate a 3D view of the vehicles surroundings. One of the challenges to this lies in getting the information needed to make split-second decisions in real-time. Eventually, 5G and artificial intelligence will be leveraged in tandem to give these vehicles a more accurate view of the road, making cars more functional and safe.
Artificial intelligence is also ushering in a new chapter for smartphones. Even though most of us dont realize it, artificial intelligence is powering many of the features on several mobile apps today. These include Map apps, as well as virtual assistants like Google Assistant, Cortana, and Siri. With mobile apps running telematics in the background, drivers gain access to the latest technologies in driver safety, artificial intelligence, and 5G in a single device. Drivers are also able to use voice commands to look for gas stations, perform internet searches, and communicate with friends and family instead of physically using their phones while driving. Even more, artificial intelligence paired with telematics gives drivers access to real-time information on fuel usage, vehicle location, driver behavior, and speed.
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The Surprising Way Artificial Intelligence Is Transforming Transportation - Forbes
Fujifilm Showcases Artificial Intelligence Initiative And Advances at RSNA 2019 – Imaging Technology News
November 30, 2019 Fujifilm Medical Systems U.S.A. is showcasing REiLI, the company's global medical imaging and informatics artificial intelligence (AI) technology initiative at the 2019 Radiological Society of North America's (RSNA) annual meeting.
"At RSNA 2019, we look forward to sharing the AI insights and advances we've made by working closely with clinical and research partners for several years," said Takuya Shimomura, chief technology officer and executive director, Fujifilm. "Ultimately, the long-term goal of our AI initiative is to help providers make better decisions that improve patient lives."
Under the REiLI brand, Fujifilm is developing AI technologies that strongly support diagnostic imaging workflow, leveraging the combination of its deep learning innovations and distinct image processing heritage. Applications currently in development include, but are not limited to: Region Recognition, an AI technology that helps to accurately recognize and consistently extract organ regions, regardless of deviations in shape, presence or absence of disease, and imaging conditions; Computer Aided Detection, an AI technology to reduce the time of image interpretation and support radiologists' clinical decision making; Workflow Support, using AI technology to realize optimal study prioritization, alert communications of AI findings, and report population automation.
"Our latest Synapse 7x brings diagnostic radiology, mammography and cardiology together on the server-side, enabling immediate interaction with these modality imaging data sets through a single AI-enabled platform," said Bill Lacy, vice president, medical informatics, Fujifilm. "We're excited to debut this solution for our U.S. customers at RSNA 2019, showing our commitment to progressing AI technology to empower physicians to make more efficient and impactful care decisions."
RSNA attendees are encouraged to learn more about REiLI at Booth #4111 and participate in the following Fujifilm-hosted activities.
At booth #4111, attendees can visit Fujifilm's AI Lab. The lab will feature dedicated workstations demonstrating REiLI use cases within Synapse PACS. Attendees can witness first-hand the speed and depth of the integrated workflows achieved by unifying Fujifilm's REiLI technology with the company's server-side PACS system. Featured in the AI lab will be Fujifilm developed algorithms, to include CT lung nodule, intracerebral hemorrhage, cerebral infarction MR and CT, spine label and bone temporal subtraction to name a few. In addition to the Fujifilm AI development, the AI lab will showcase its strengths by supporting a multitude of integration points in support of partner vendor and provider developed algorithms. This will include Riverain's lung nodule, MaxQ's stroke, Lunit's Chest and 2-D Mammography, LPixel's MR Aneurysm, Koios' US breast, Aidoc's pulmonary embolism and Gleamer's bone fracture.
For more inform rsna.fujimed.com
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Artificial intelligence in FX ‘may be hype’ – FX Week
AI talk: FX Week Europe panellists dont see much use for complex machine learning in FX
Artificial intelligence can be particularly useful in asset classes where there are thousands of instruments available to trade, but it is not deemed as practical in a market such as foreign exchange, where the overall number of currency pairs is limited and even less so in the majors, remarked panellists at the 2019 FX Week Europe conference.
While the panellists did not completely disregard the potential for AI in FX, they did not believe it is as relevant as it is for equities, for example.
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SC Proposes Introduction Of Artificial Intelligence In Justice Delivery System – Inc42 Media
AI will help in better administration of justice delivery and constitution, says Chief Justice Of India SA Bobde
Automation will, however, not replace humans, CJI adds
Authorities will continue to use human translators to validate and correct output of AI-based tools
After creating waves across startups, artificial intelligence (AI) seems to have now entered the doors of justice. The Chief Justice of India, SA Bobde, has recently said that the Supreme Court has proposed to introduce a system of AI which would help in better administration of justice delivery and constitution.
The CJI also made it clear that the automation would not replace humans. He said that the judiciary would continue to rely on the knowledge and wisdom of judges and the deployment of an AI integration would help reduce the number of pending cases and improve the efficiency of the judicial system.
We propose to introduce, if possible, a system of artificial intelligence. There are many things which we need to look at before we introduce ourselves. We do not want to give the impression that this is ever going to substitute the judges, said the CJI at the Constitution Day function organised by the Supreme Court Bar Association.
Reiterating how automation would not take away jobs, Justice Bobde said the law functions in a uniquely complex environment that lawyers and judges are best placed to navigate. He also said that the authorities would continue to use human translators to validate and correct the output of the AI-based translation tools.
Union Law Minister Ravi Shankar who was also present at the Constitution Day function said that India had started a startup movement in 2015 and India has now become the third-largest country in terms of startups. He also said that more than 24K startups have come up since 2015 out of which more than 10K are startups on information technology.
Indian startups have been changing the face of many industries including ecommerce marketing, banking, healthcare, fintech among others by deploying AI.
Bill Gates, who was on a three-day visit to India, also spoke about how startups in India have revolutionized the healthcare sector by using automation. Gates spoke about how smartphones are changing how chronic diseases are detected. He also added that this kind of technology has helped the world make a lot of progress. We have miracle tools provided by current AI, like cancer detection. What we have today is a tool that can take AI and create an ultrasound device where when a woman is pregnant is going to see if it is going to be a complicated pregnancy, he added.
With hundreds of companies across verticals moving their data to the cloud, AI has become very important and Indians are contributing a lot towards the global AI ecosystem. The government is also looking to solve bring huge data sets to the public domain for startups to leverage and create solutions in more sectors.
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SC Proposes Introduction Of Artificial Intelligence In Justice Delivery System - Inc42 Media
How Augmented Reality and Artificial Intelligence Are Helping Entrepreneurs Create a Better Customer Experience – Entrepreneur
Expert insights on taking personalization to the next level.
November25, 20194 min read
Opinions expressed by Entrepreneur contributors are their own.
Michael Bower helps companies provide cool experiences to their customers on the web. As CEO of Sellry, an ecommerce solutions company, he combines creativity with the latest technology to propel brands into the future. Alongside clients, Sellry works to reimagineand designthe future of ecommerce.
What new technology do you think will greatly impact consumer-facing startups in the near future?
AR is going to completely change many industries. We've seen applications where you can just point your phone at something and it'll tell you about it. We've also seen smart mirrors. There's even APIs where it'll measure your body from a photograph with a degree of accuracy. A lot of these APIs are nearly real-time. Some of them can even look at multiple different subjects at the same time and figure out many things about them. It's the future.
Related:The Future ofAugmented Reality(Infographic)
How soon do you think this will be a common practice?
We did an experiment this year. We built out an augmented reality experience of an imaginary office space for an ecommerce trade show. We wanted to see how relatable it was.Would people get it? Would they understand it? And what we found was, it's still a little bit early. Enterprises are toying with the idea, some of them are trying things, especially in the sports and entertainment industries. Fashion is obviously trying things for sizing. I think that we're looking at 2021 for when we pass that early adopter stage and start getting into the early majority.
What industry do you think will be the first to benefit from AR?
I think certain industries like real estate, architecture and B2B sales will adopt it faster because AR will give them the ability in the fields to conduct a demonstration or to evaluate a pitch better. There are enormous companies in those spaces already investing absolutely insane amounts of money into AR.
What about artificial intelligence? How are companies using it to enhance the customer experience?
If you've ever looked at the cookies that are stored on your machine, they're crazy. Some of them will think that you're probably into things that you're totally not into. I've looked at my cookies and been like, Wow, they think I'm interested in soap operas, which I'm totally not. Cookies are notoriously unreliable. And that's what most people are using for advertising and retargeting. Basically, its a "spray and pray" approach. What we want to do is help companies take better advantage of their audience, the people that are on their site and telling them real things about themselves.
Related:4EcommerceTrends to Watch
Can you give an example?
Let's say that we're dealing with a supplements company. Right now, we're segmenting based on a few factors, and we think we know who our customer is. And we've done a lot of testing that is assumption based. Meaning we're taking things that we already know and we're using that to drive our decision-making. Now, the AI tooling for this stuff is already in principle there, where you can just turn on artificial intelligence and it'll figure out who your customer is, how you should message them, what is the cadence of doing that. But right now for the mid-market and even for certain specialized enterprise markets, the AI tooling takes a long time to deploy, so it's not quite there all the way in a deployable manner.Within a couple of years it will be.
How can companies that currently dont use data science prepare to implement artificial intelligence as it becomes more widely available?We encourage companies to really dial into customer discovery and understanding the customer deeply. And then build out a higher fidelity version of current generation personalization and segmentation going on. And then based on that, within the next couple of years we're wanting to have the ability to deploy for our clients technical wizardry that's going to basically take those human-defined segments and personas, and take them even farther. AI-based segmentation and the ability for the mid-market to adopt AI is going to be super amazing and exciting.
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Artificial Intelligence of Things (AIoT) Market Research Report 2019-2024 – Embedded AI in Support of IoT Things/Objects Will Reach $4.6B Globally by…
Dublin, Nov. 27, 2019 (GLOBE NEWSWIRE) -- The "Artificial Intelligence (AI) in Big Data, Data as a Service (DaaS), AI Supported IoT (AIoT), and AIoT DaaS 2019 - 2024" report has been added to ResearchAndMarkets.com's offering.
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This Artificial Intelligence of Things (AIoT) market research provides analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2019 through 2024. It also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service (DaaS), Decisions as a Service, and the market for AIoT in smart cities.
This research also evaluates various AI technologies and their use relative to analytics solutions within the rapidly growing enterprise and industrial data arena. It assesses emerging business models, leading companies, and solutions. It also analyzes how different forms of AI may be best used for problem solving. The report also evaluates the market for AI in IoT networks and systems. This research provides forecasting for unit growth and revenue for both analytics and IoT. It includes an evaluation of the technologies, companies, and solutions for leveraging big data tools and advanced analytics for IoT data processing. Emphasis is placed on leveraging IoT data for process improvement, new and improved products, and ultimately enterprise IoT data syndication. It includes detailed forecasts for 2019 through 2024.
This research also evaluates the technologies, companies, strategies, and solutions for DaaS. It assesses business opportunities for enterprise use of own data, others data, and a combination of both. It also analyzes opportunities for enterprises to monetize their own data through various third-party DaaS offerings. This research also evaluates opportunities for DaaS in major industry verticals as well as the future outlook for emerging data monetization from 2019 to 2024.
It is important to recognize that intelligence within IoT technology market is not inherent but rather must be carefully planned. AIoT market elements will be found embedded within software programs, chipsets, and platforms as well as human-facing devices such as appliances, which may rely upon a combination of local and cloud-based intelligence.
Just like the human nervous system, IoT networks will have both autonomic and cognitive functional components that provide intelligent control as well as nerve end-points that act like nerve endings for neural transport (detection and triggering of communications) and nerve channels that connect the overall system. The big difference is that the IoT technology market will benefit from engineering design in terms of Artificial Intelligence (AI) and cognitive computing placement in both centralized and edge computing locations.
AI is rapidly making its way into many advanced solutions including autonomous vehicles, smart bots, advanced predictive analytics, and more. Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery and support models. The term for AI support of IoT (or AIoT) is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination.
AI enhances the ability for big data analytics and IoT platforms to provide value to each of these market segments. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.
The convergence of AI and IoT technologies and solutions (AIoT) is leading to thinking networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange.
AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange.
While early solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.
In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will therefore be leveraged to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI based Decisions as a Service.
IoTDaaS constitutes retrieving, storing and analyzing information and provide customer either of the three or integrated service package depending on the budget and the requirement. New models are emerging to reduce friction across the value chain including enhanced Big Data as a Service (BDaaS) offerings. BDaaS is anticipated to make cross-industry, cross-company, and even cross-competitor data exchange a reality that adds value across the ecosystem with minimized security and privacy concerns.
IoTDaaS offers convenient and cost effective solutions to enterprises of various sizes and domain. IoTDaaS constitutes retrieving, storing and analyzing information and provide customer either of the three or integrated service package depending on the budget and the requirement. AI algorithms enhance the ability for big data analytics and IoT platforms to provide value to each of these market segments. One of the important growth areas for the Data as a Service market is to leverage AI to offer Value-added Data in a Decisions as a Service model.
Big data in IoT is different than conventional IoT and thus will requires more robust, agile and scalable platforms, analytical tools and data storage systems than conventional big data infrastructure. Looking beyond data management processes, IoT data itself will become extremely valuable as an agent of change for product development as well as identification of supply gaps and realization of unmet demands. Big data and analytics will increase in importance as IoT evolves to become more commonplace with the deployment of 5G IoT.
The Massive Machine-type Communications (mMTC) portion of fifth generation cellular networks will facilitate a highly scalable M2M network for many IoT applications, particularly those that do not require high bandwidth. Data generated through sensors embedded in various things/objects will generate massive amounts of unstructured (big) data on real-time basis that holds the promise for intelligence and insights for dramatically improved decision processes.
Big data in IoT is also dissimilar than non-machine related analytics and thus will require more robust, agile and scalable platforms, analytics tools, and data storage systems than conventional infrastructure. Due to this new architecture approach, the need to handle data differently, and the sheer volume of unstructured data, there will be great opportunities for big Data in IoT. Analytics used in IoT will become an enabler for the entire IoT ecosystem as enterprise begins to take advantage of new business opportunities such as syndicating their own data.
AI coupled with advanced big data analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.
These advanced analytics provide the ability to make raw data meaningful and useful as information for decision-making purposes. AI enhances the ability for big data analytics and IoT platforms to provide value to each of these market segments. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.
The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service. However, real-time data is anticipated to become a highly valuable aspect of all solutions as a determinant of user behavior, application effectiveness, and identifier of new and enhanced mobile/wireless and/or IoT related apps and services.
In terms of overall AIoT data management, we see three different types of IoT Data:
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AI will be useful in support of managing each of these data types in terms of identifying, categorizing, and decision making.
We see the AIoT market transforming from today's largely consumer appliance and electronics related approach to one in which AIoT data is highly valued asset wherein companies like SAS provide a utility function in terms of helping enterprise, industrial, and government clients monetize their data. This will likely occur in a Data as a Service market model, which may be segmented in various ways including by Sector including Public Data, Business Data, and Government Data:
It may also be segmented by Source Type. As it is prohibitively difficult to identify all of the sources and source types, we have broadly segmented Source by Machine Data (consumer appliances, vehicles [ cars, trucks, planes, trains, ships, etc. ], robots and industrial equipment, etc.) and Non-machine Data (everything else including people texting/talking/etc., enterprise data collected by humans, etc.).
It is important to note that the DaaS also includes data sourced from a machine (such as from a jet engine) that is not Internet-connected and thus limited in utility without the Internet of Things (IoT) to collect, relay, and provide opportunities for feedback loops. Accordingly, we have also segmented the Data as a Service market by Data Collection Type, which includes IoT DaaS data and Non-IoT DaaS data. Machine Data that does not use IoT, by definition, will not be streaming data or allow for real-time analytics.
Research Benefits
Key Topics Covered
Artificial Intelligence of Things: AIoT Market by Technology and Solutions1. Executive Summary2. Introduction3. AIoT Technology and Market4. AIoT Applications Analysis5. Analysis of Important AIoT Companies6. AIoT Market Analysis and Forecasts 2019 - 20247. Conclusions and Recommendations
Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services1. Executive Summary2. Introduction3. Overview4. AI Technology in Big Data and IoT5. AI Technology Application and Use Case6. AI Technology Impact on Vertical Market7. AI Predictive Analytics in Vertical Industry8. Company Analysis9. AI in Big Data and IoT Market Analysis and Forecasts 2019 - 202410. Conclusions and Recommendations11. Appendix
Big Data in Internet of Things: IoT Data Management, Analytics, and Decision Making1. Executive Summary2. Big Data in Internet of Things3. Big Data in IoT Business Trends and Predictions4. Big Data in IoT Vendor Ecosystem5. Big Data in IoT Market Analysis and Forecasts6. Key Companies7. Summary and Conclusions
Data as a Service Market by Enterprise, Industrial, Public and Government Data Applications and Services1. Executive Summary2. Data as a Service Technologies3. Data as a Service Market4. Data as a Service Strategies5. Data as a Service Applications6. Market Outlook and Future of Data as a Service7. Data as a Service Market Analysis and Forecasts 2019 - 20248. Regional DaaS Market Analysis and Forecasts 2019 - 20249. Conclusions and Recommendations10. Appendix
For more information about this report visit https://www.researchandmarkets.com/r/ukr9g6
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The Best Artificial Intelligence Stocks of 2019 — and The Top AI Stock for 2020 – The Motley Fool
Artificial intelligence (AI) -- the capability of a machine to mimic human thinking and behavior -- is one of the biggest growth trends today.Spending on AI systems will increase by more than two and a half times between 2019 and 2023, from $37.5 billion to $97.9 billion, for a compound annual growth rate of 28.4%,according to estimates by research firm IDC. Other sources are projecting even more torrid growth rates.
There are two broad ways you can get exposure to the AI space:
With this background in mind, let's look at which AI stocks are performing the best so far this year (through Nov. 25) and which one is my choice for best AI stock for 2020.
Image source: Getty Images.
The following chart isn't meant to be all-inclusive, as that would be impossible, and the chart has limits on the number of metrics. Notable among the companies missing areAdvanced Micro Devices and Intel. They were left out largely because NVIDIA is currently the leader in supplying AI chips. While there are things to like about shares of both of these companies, NVIDIA stock is the better play on AI, in my view.
Data by YCharts.
Graphics processing unit (GPU) specialist NVIDIA (NASDAQ:NVDA), e-commerce and cloud computing service titanAmazon, computer software and cloud computer service giant Microsoft, Google parent and cloud computing service provider Alphabet, old technology guard and multifaceted AI player IBM, and Micron Technology, which makes computer memory chips and related storage products, would best be put in the first category above. They produce and sell AI-related products and/or services. They're all also probably using AI internally, with Amazon and Alphabet being notably heavy users of the tech to improve their products.
iPhone makerApple (NASDAQ:AAPL), social media leader Facebook (NASDAQ:FB), video-streaming king Netflix, and Stitch Fix, an online personal styling service provider, would best be categorized in the second group since they're either primarily or solely using AI to improve their products and services.
Now let's look at some basic stats for the three best performers of this group.
Company
Market Cap
P/E(Forward)
Wall Street's 5-Year Estimated Average Annual EPS Growth
5-Year Stock Return
Apple
NVIDIA
S&P 500
--
--
Data sources: YCharts (returns) and Yahoo! Finance (all else). P/E = price-to-earnings ratio. EPS = earnings per share. Data as of Nov. 25, 2019.
On a valuation basis alone, Facebook stock looks the most compelling when we take earnings growth estimates into account. Then would come Apple and then NVIDIA. However, there are other factors to consider, with the biggie being that projected earnings growth is just that, projected.
There's a good argument to be made that NVIDIA has a great shot at exceeding analysts' earnings estimates. Why? Because it has a fantastic record of doing so, and all one needs to do is listen to enough quarterly earnings calls with Wall Street analysts to realize why this is so: A fair number of them don't seem to have a strong grasp of the company's operations and products. (I'm not knocking, as most analysts don't have technical backgrounds, and they cover a lot of companies.)
Facebook stock probably has the potential to continue to be a long-term winner. But it's relatively high regulatory risk profile makes it not a good fit for all investors. Moreover, it will likely have to keep spending a ton of money to help prevent "bad actors" from using its site for various nefarious purposes. Indeed, this is one of the major internal functions for which the company is using AI. It also uses the tech to recognize and tag uploaded images, among other things.
Apple uses AI internally in various ways, with the most consumer-facing one being powering its voice assistant Siri. It's the best of these three stocks for more conservative investors, as it has a great long-term track record and pays a modest dividend.NVIDIA, however, is probably the better choice for growth-oriented investors who are comfortable with a moderate risk level.
Image source: Getty Images.
NVIDIA is the leading supplier of graphics cards for computing gaming, with AMD a relatively distant second. In the last several years, it's transformed itself into a major AI player, or more specifically, a force to be reckoned with in the fast-growing deep-learning category of AI. Its GPUs are the gold standard for AI training in data centers, and it's now making inroads into AI inferencing. (Inferencing involves a machine or device applying what it's learned in its training to new data. It can be done in data centers or "at the edge" -- meaning at the location of the machine or device that's collecting the data.)
NVIDIA is in the relatively early stages of profiting from many gigantic growth trends, including AI, esports, driverless vehicles, virtual reality (VR), smart cities, drones, and more. (There is some overlap in these categories, as AI is involved to some degree in most of NVIDIA's products.) There are no pure plays on AI, to my knowledge, but NVIDIA would probably come the closest.
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The Best Artificial Intelligence Stocks of 2019 -- and The Top AI Stock for 2020 - The Motley Fool
It Pays To Break Artificial Intelligence Out Of The Lab, Study Confirms – Forbes
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Yes, artificial intelligence (AI) is proving itself to be a worthwhile tool in the business arena at least in focused, preliminary projects. Intelligent chatbots are a classic example. Now its a question of how quickly it can be expanded to deliver on a wider basis across the business to automate decisions around inventory or investments, for example.
Theres progress on this front, as shown in McKinseys latest survey of 2,360 executives, which shows a nearly 25 percent year-over-year increase in the use of AI in various business processes and there has been a sizable jump in companies spreading AI across multiple processes.
A majority of executives in companies that have adopted AI report that it has increased revenues in areas where it is used, and 44 percent say it has reduced costs, the surveys authors, Arif Cam, Michael Chui, and Bryce Hall, all with McKinsey, state.
The results also show that a small share of companies the authors call them AI high performers are attaining outsize business results from AI. Close to two in three companies, 63 percent, report revenue increases from AI adoption in the business units. Respondents from high performers are nearly three times likelier than their lagging counterparts to report revenue gains of more than 10 percent, the survey shows.
The leading AI use cases include marketing and sales, product and service development, and supply-chain management. In marketing and sales, respondents most often report revenue increases from AI use in pricing, prediction of likelihood to buy, and customer-service analytics, the surveys authors report. In product and service development, revenue-producing use cases include the creation of new AI-based products and new AI-based enhancements. And in supply-chain management, respondents often cite sales and demand forecasting and spend analytics as use cases that generate revenue.
What are these high performers doing differently? Strategy is a key area. For example, 72 percent of respondents from AI high performers say their companies AI strategy aligns with their corporate strategy, compared with 29 percent of respondents from other companies. Similarly, 65 percent from the high performers report having a clear data strategy that supports and enables AI, compared with 20 percent from other companies. Also, the application of standardized tools to be used across the enterprise is more likely to be seen at high performers.
Adoption of Strategic AI Approaches:
Retraining workers is also a key differentiator, the survey shows. One-third of high performers, 33%, indicate the majority of their workforce has received AI-related training over the past year, compared to five percent of lagging organizations. Over the next three years, 42% of high performers intend to extend such training to most of their workers, versus only 17% of their lagging counterparts.
For AI to take hold, the McKinsey authors urge ramping up workforce retraining. Even the AI high performers have work to do in several key areas, the surveys authors point out. Only 36 percent of respondents from these companies say their frontline employees use AI insights in real time for daily decision making. A minority, 42 percent, report they systematically track a comprehensive set of well-defined key performance indicators for AI. Likewise, only 35 percent of respondents from AI high performers report having an active continuous learning program on AI for employees.
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It Pays To Break Artificial Intelligence Out Of The Lab, Study Confirms - Forbes
Artificial intelligence will affect Salt Lake, Ogden more than most areas in the nation, study shows – KSL.com
SALT LAKE CITY The Salt Lake and Ogden-Clearfield areas are among the top 10 regions in the United States that will be most affected by the rise of artificial intelligence, according to a study recently released by Washington D.C.-based research group the Brookings Institution.
In the past, research has suggested that AI will disproportionately affect blue-collar and low-income workers, like factory employees or office clerks, who will soon find themselves replaced by machines. But past research hasnt often distinguished between the coming effects of advancements in robotics and software, and those of artificial intelligence, or computers that can plan, learn, reason and problem solve.
As robotics and software become more sophisticated, theyll replace employees in industries like manufacturing, construction or clerical work, the study claims. But artificial intelligence will change the world of the white-collar worker more than anything else and Salt Lake and Ogden will be in the thick of it.
In fact, AI will disproportionately affect areas that specialize in industries like technology, engineering, science, transportation, manufacturing and law, the study shows. And Utahs booming tech sector has not gone unnoticed.
Among the most AI-exposed large metro areas are San Jose, Calif., Seattle, Salt Lake City and Ogden, Utah all high-tech centers, the study reads.
Those four tech hubs are joined in the top 10 most-affected areas by agriculture, logistics and manufacturing centers like Bakersfield, California; Greenville, South Carolina; Detroit, Michigan; and Louisville, Kentucky.
Higher educated and higher paid workers will be most affected by the rise of AI in the coming decades, and workers with bachelors degrees will be more than five times as exposed to artificial intelligence as workers with high school degrees, the study shows.
Eventually, AI will be a significant factor in the future work lives of relatively well-paid managers, supervisors and analysts, according to the report.
Nobody can predict the future, said Dan Ventura, a computer science professor at Brigham Young University who specializes in artificial intelligence research.
While the studys methodology and predictions are kind of cool and better than nothing, theyre just that: predictions, Ventura explained. And the study acknowledges its shortcomings, too.
While the present assessment predicts areas of work in which some kind of impact is expected, it doesnt specifically predict whether AI will substitute for existing work, complement it, or create entirely new work for humans, the study reads.
AI is getting disturbingly good at pattern recognition and pattern matching, including tasks like facial recognition or medical diagnosing from images, Ventura said. But it falls short in other areas.
AI is not good at judgement right now. And even to the extent that it is good at judgement, people dont trust it and dont know if they can trust it. So theyre not going to turn that kind of thing over to AI. At least, they shouldnt, he said.
So while Ventura believes jobs that require skills like pattern recognition may be threatened, those that involve judgment calls are probably safe for a while.
Whats interesting about this (study) is the claim that theyre making that, probably for the first time, this sort of displacement concern, it isnt focused on lower education, lower skill its the other kind of people that theyre worried about. And I think thats pretty interesting, even if Im not sure I buy it all the way, he said.
Ventura does predict, however, that even if AI replaces certain high-skill jobs, new jobs will pop up in response. The rise of artificial intelligence will most likely require (at least in the beginning) something like AI quality control to ensure that the new technology isnt making mistakes.
AI is not good at judgement right now. And even to the extent that it is good at judgement, people dont trust it.Dan Ventura, BYU computer science professor
And while the rise of AI may cause some workforce casualties along the way, Ventura expects the labor market will adapt to the technological advancements, as it has throughout all of human history.
Mark Knold, chief economist of Utahs Department of Workforce Services, agrees.
His research shows that there simply arent enough workers to maintain the size of the U.S. economy as it stands. Instead, the labor market must either allow more immigrants into the country, let the economy shrink in size, or let machines do some of the work, he said.
Artificial intelligence wont replace workers, it will replace missing workers, he argues.
"A lot of these studies can leave you the impression with a fear of the future, Knold said. I think thats the wrong takeaway from studies like this. Theres always new technologies coming that threaten old technologies and workers in those old technologies. But yet, as time goes on, they transition to the new ones, and things are even bigger and better.
If workers are going to be ready to adapt to the change artificial intelligence brings to the workforce, education will need to adapt too, Ventura explained.
But the BYU professor believes the states educational system is already behind, even at the university level.
In my little computer science environment, were not out of touch with it at all, he said. But if you look at the general education program (at BYU), theres nothing. Theres no computer science (or) algorithmic stuff in general education. Its just not a thing.
Utahs fast-growing tech companies have been aware of a talent gap for awhile as they scramble to find employees to fill their ever-expanding needs. But research shows that unless children are exposed to computer science at an early age, theyre much less likely to choose it as a career.
While Utah is working to bring computer science to all K-12 schools in the state by 2022, its a difficult feat, and educational curriculums dont change nearly as fast as technology.
If this AI boom continues to happen, and technology continues to march forward, and we see some of these paradigm-shifting kinds of things, thatll just make us even more behind, Ventura said.
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2019 Artificial Intelligence in Precision Health – Dedication to Discuss & Analyze AI Products Related to Precision Healthcare Already Available -…
DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Precision Health" book from Elsevier Science and Technology has been added to ResearchAndMarkets.com's offering.
Artificial Intelligence in Precision Health: From Concept to Applications provides a readily available resource to understand artificial intelligence and its real time applications in precision medicine in practice. Written by experts from different countries and with diverse background, the content encompasses accessible knowledge easily understandable for non-specialists in computer sciences. The book discusses topics such as cognitive computing and emotional intelligence, big data analysis, clinical decision support systems, deep learning, personal omics, digital health, predictive models, prediction of epidemics, drug discovery, precision nutrition and fitness. Additionally, there is a section dedicated to discuss and analyze AI products related to precision healthcare already available.
Key Topics Covered:
Section 1: Artificial Intelligence Technologies 1. Interpretable Artificial Intelligence: Addressing the Adoption Gap in Medicine 2. Artificial Intelligence methods in computer-aided diagnostic tools and decision support analytics for clinical informatics 3. Deep learning in Precision Medicine 4. Machine learning systems and precision medicine: a conceptual and experimental approach to single individual statistics 5. Machine learning in digital health, recent trends and on-going challenges 6. Data Mining to Transform Clinical and Translational Research Findings into Precision Health
Section II: Applications and Precision Systems/Application of Artificial Intelligence 7. Predictive Models in Precision Medicine 8. Deep Neural Networks for Phenotype Prediction: Application to rare diseases 9. Artificial Intelligence in the management of patients with intracranial neoplasms 10. Artificial Intelligence to aid the early detection of Mental Illness 11. Use of Artificial Intelligence in Alzheimer Disease Detection 12. Artificial Intelligence to predict atheroma plaque vulnerability 13. Decision support systems in cardiovascular medicine through artificial intelligence: applications in the diagnosis of infarction and prognosis of heart failure 14. Artificial Intelligence for Decision Support Systems in Diabetes 15. Clinical decision support systems to improve the diagnosis and management of respiratory diseases 16. Use of Artificial Intelligence in Neurosurgery and Otorhinolaryngology (Head and Neck Surgery) 17. Use of Artificial Intelligence in Emergency Medicine 18. Use of Artificial Intelligence in Infectious diseases 19. Artificial Intelligence techniques applied to patient care and monitoring 20. Use of artificial intelligence in precision nutrition and fitness
Section III: Precision Systems 21. Artificial Intelligence in Precision health: Systems in practice
Authors
For more information about this book visit https://www.researchandmarkets.com/r/i5n12k
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