Category Archives: Artificial Intelligence

CHICAGO, June 15, 2022 /PRNewswire/ --According to the new market research report "AI in Drug Discovery Market by Offering (Software, Service), Technology (Machine Learning, Deep Learning), Application (Cardiovascular, Metabolic, Neurodegenerative), End User (Pharma, Biotech,CROs) - Global Forecasts to 2027", published by MarketsandMarkets, the global Artificial Intelligence in Drug Discovery Market is projected to reach USD 4.0 billion by 2027 from USD 0.6 billion in 2022, at a CAGR of 45.7% during the forecast period.

Browse in-depth TOC on "Artificial Intelligence (AI) in Drug Discovery Market"

177 Tables33 Figures198 Pages

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=151193446

The growth of this Artificial Intelligence in Drug Discovery Market is driven by the growing need to control drug discovery & development costs, and growing number of cross-industry collaborations and partnerships, On the other hand, a lack of data sets in the field of drug discovery and the inadequate availability of skilled labor are some of the factors challenging the growth of the market.

Services segment is expected to grow at the highest rate during the forecast period.

Based on offering, the AI in drug discovery market is segmented into software and services. In 2021, the services segment accounted for the largest market share of the global AI in drug discovery services market and also expected to grow at the highest CAGR during the forecast period. The benefits associated with AI services and the strong demand for AI services among end users are the key factors driving the growth of this market segment.

Machine learning technology segment accounted for the largest share of the global AI in drug discovery market.

Based on technology, the AI in drug discovery market is segmented into machine learning and other technologies. The machine learning segment accounted for the largest share of the global market in 2021 and expected to grow at the highest CAGR during the forecast period. The machine learning technology segment further segmented into deep learning, supervised learning. reinforcement learning, unsupervised learning, and other machine learning technologies. Deep learning segment accounted for the largest share of the market in 2021, and this segment also expected to grow at the highest CAGR during the forecast period.

Request Sample Pages: https://www.marketsandmarkets.com/requestsampleNew.asp?id=151193446

The immuno-oncology application segment accounted for the largest share of the AI in drug discovery market in 2021.

On the basis of application, the AI in drug discovery market is segmented into neurodegenerative diseases, immuno-oncology, cardiovascular diseases, metabolic diseases, and other applications. The immuno-oncology segment accounted for the largest share of the market in 2021, owing to the increasing demand for effective cancer drugs. The neurodegenerative diseases segment is estimated to register the highest CAGR during the forecast period. The role of AI in resolving existing complexities in neurological drug development and strategic collaborations between pharmaceutical companies & solution providers are the key factors responsible for the high growth rate of the neurodegenerative diseases segment.

Pharmaceutical & biotechnology companies segment accounted for the largest share of the global AI in drug discovery market.

On the basis of end user, the AI in drug discovery market is segmented into pharmaceutical & biotechnology companies, CROs, and research centers and academic & government institutes. The pharmaceutical & biotechnology companies segment accounted for the largest market share of AI in drug discovery market, in 2021, while the research centers and academic & government institutes segment is projected to register the highest CAGR during the forecast period. The strong demand for AI-based tools in making the entire drug discovery process more time and cost-efficient is driving the growth of this end-user segment.

Speak to Analyst: https://www.marketsandmarkets.com/speaktoanalystNew.asp?id=151193446

North America is expected to dominate the Artificial Intelligence in Drug Discovery Marketin 2022.

North America accounted for the largest share of the global AI in drug discovery market in 2021 and also expected to grow at the highest CAGR during the forecast period. North America, which comprises the US, Canada, and Mexico, forms the largest market for AI in drug discovery. These countries have been early adopters of AI technology in drug discovery and development. Presence of key established players, well-established pharmaceutical and biotechnology industry, and high focus on R&D & substantial investment are some of the key factors responsible for the large share and high growth rate of this market

Prominent players in this Artificial Intelligence in Drug Discovery Marketare NVIDIA Corporation (US), Microsoft Corporation (US), Google (US), Exscientia (UK), Schrdinger (US), Atomwise, Inc. (US), BenevolentAI (UK), NuMedii (US), BERG LLC (US), Cloud Pharmaceuticals (US), Insilico Medicine (US), Cyclica (Canada), Deep Genomics (Canada), IBM (US), BIOAGE (US), Valo Health (US), Envisagenics (US), twoXAR (US), Owkin, Inc. (US), XtalPi (US), Verge Genomics (US), Biovista (US), Evaxion Biotech (Denmark), Iktos (France), Standigm (South Korea), and BenchSci (Canada). Players adopted organic as well as inorganic growth strategies such as product upgrades, collaborations, agreements, partnerships, and acquisitions to increase their offerings, cater to the unmet needs of customers, increase their profitability, and expand their presence in the global market.

Browse Adjacent Markets: Healthcare IT Market Research Reports& Consulting

Browse Related Reports:

Drug Discovery Services Market by Process (Target Selection, Validation, Hit-to-lead), Type (Chemistry, Biology), Drug Type (Small molecules, biologics), Therapeutic Area (Oncology, Neurology) End User (Pharma, Biotech) - Global Forecast to 2026

https://www.marketsandmarkets.com/Market-Reports/drug-discovery-services-market-138732129.html

Artificial Intelligence In Genomics Market by Offering (Software, Services),Technology (Machine Learning, Computer Vision), Functionality (Genome Sequencing, Gene Editing), Application (Diagnostics), End User (Pharma, Research)-Global Forecasts to 2025

https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-in-genomics-market-36649899.html

About MarketsandMarkets

MarketsandMarkets provides quantified B2B research on 30,000 high growth niche opportunities/threats which will impact 70% to 80% of worldwide companies' revenues. Currently servicing 7500 customers worldwide including 80% of global Fortune 1000 companies as clients. Almost 75,000 top officers across eight industries worldwide approach MarketsandMarkets for their painpoints around revenues decisions.

Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. MarketsandMarkets now coming up with 1,500 MicroQuadrants (Positioning top players across leaders, emerging companies, innovators, strategic players) annually in high growth emerging segments. MarketsandMarkets is determined to benefit more than 10,000 companies this year for their revenue planning and help them take their innovations/disruptions early to the market by providing them research ahead of the curve.

MarketsandMarkets's flagship competitive intelligence and market research platform, "Knowledge Store" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets.

Contact:

Mr. Aashish MehraMarketsandMarkets INC.630 Dundee RoadSuite 430Northbrook, IL 60062USA: +1-888-600-6441Email: sales@marketsandmarkets.comResearch Insight: https://www.marketsandmarkets.com/ResearchInsight/ai-in-drug-discovery-market.aspVisit Our Web Site: https://www.marketsandmarkets.comContent Source: https://www.marketsandmarkets.com/PressReleases/ai-in-drug-discovery.asp

Logo: https://mma.prnewswire.com/media/660509/MarketsandMarkets_Logo.jpg

SOURCE MarketsandMarkets

Read more from the original source:

Artificial Intelligence (AI) in Drug Discovery Market worth $4.0 billion by 2027 - Exclusive Report by MarketsandMarkets - PR Newswire UK

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

Artificial intelligence (AI) technologies are considered essential for internet of things (IoT) hardware for digital operations, such as cameras and automation equipment, according to a survey from Samsara released today.

Samsara, which makes IoT hardware and software, surveyed more than 1,500 operations leaders for its 2022 State of Connected Operations survey, in industries including transportation, manufacturing, construction, field services and food and beverage. The survey was conducted by the independent research firm Lawless Research.

Organizations with physical operations represent more than 40% of global gross domestic product, yet theyve been historically underserved by technology, said Stephen Franchetti, Samsaras CIO.

The IoT market is booming: A March 2020 Insider Intelligence report, for example, predicted that the IoT market size would reach more than $2 trillion by 2027.

The pandemics supply chain interruptions have only underpinned the need for increased investment in IoT. For instance, in late 2021, when the effects of the pandemic were already being felt, the market research firm Gartner discovered industrial enterprises were speeding investments in industrial IoT (IIoT) platforms to improve business and industrial processes.

The IoT and IIoT acronyms are widely used interchangeably, though the IoT is generally applicable to consumer and home devices, such as thermostats and lights, while the IIoT connects physical industrial systems. It also analyzes data returned from those systems for operational improvement.

In industry, the IIoT monitors conditions on, for example, a manufacturing line and predicts which machines will soon need maintenance, among other uses. It unlocks data that was previously housed in data silos, Gartner says.

And its vital to Industry 4.0 adoption, according to McKinsey. The technology holds the key to unlocking drastic reductions in downtimes, new business models, and a better customer experience, the consulting company reports.

Ninety percent of respondents to the Samsara survey said they implemented or plan to implement AI automation technologies connected via the IoT.

AI and automation will play a significant role in the safety and efficiency of physical operations and were already seeing this with our customers today, Franchetti said.

In fact, 95% of those surveyed said AI and automation efforts led to increased employee retention, he said.

Our research found that 31% of respondents benefited from less time spent on repetitive tasks and 40% higher employee engagement as a result of AI and automation, he explained.

Franchetti pointed to Chalk Mountain Services, a transportation and logistics provider in the oilfield services industry. The company rolled out Samsaras AI Dash Cams across its fleet last year to study how drivers safely handled real-world conditions. With that information, the company changed how it rewarded, coached and protected drivers.

The changes translated to a 15% improvement in driver retention and an 86% decrease in preventable accident costs, Franchetti said.

Whats significant about our research is we found that early adopters of digital technologies are proving to be more agile and resilient, he said. While pen-and-paper management is still a stark reality for many companies, they can now clearly see the benefits of digitization from their industry peers.

The combination of AI tools and IoT hardware, particularly when it comes to connecting digital operations, shows no signs of slowing down over the next few years, so organizations should be prepared. These technologies will be widespread soon, and operations leaders should see them as a critical tool in defining their future of work, Franchetti said.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Learn more about membership.

Read the original:

Big business benefits from artificial intelligence in IoT & IIoT hardware - VentureBeat

AI is everywhere. Machine learning is, by implication great, because it means we dont have to do any learning ourselves. Deep Learning is the new alternative to us actually doing it, and Neural Networks are an incredibly technical alternative to what our brains do in their sleep. All this, just to save us from thinking.

In photography, AI is used for everything from selecting objects to choosing a preset, from working out what to focus on to optimizing the camera settings to suit a scene. Its about making a machine do something so that we dont have to.

AI can, indeed, do a lot of complicated things a lot faster than we can. This can mean focusing on a human eye, or a bird, or a train, or a blueberry (soon, probably) faster than we ever could. Its easy to see how that could be useful. It's leading to a revolution in sports and wildlife photography and the ability to track fast moving subjects and keep them in focus.

This isn't actually that new. Cameras have had subject-based tracking modes for a while you identify an object or an area and the camera can track it within the scene. What's changed is that cameras now know what these objects of interest are (many of them) and can locate them for you in the frame. This has gone along with much more powerful AF algorithms and faster and more powerful AF actuators in lenses.

The second is that the latest photo editing AI can save us from the effort of making complex and difficult selections manually. Skylum Luminar leads the field here, with AI-powered sky selection and replacement, AI face and feature recognition for portrait enhancement and AI subject and background masking.

Other software is doing the same. Photoshop has its own AI sky replacement feature and Lightroom has an extremely effective sky and subject masking tool that's uncannily and unerringly accurate.

So far so good. AI can definitely help us achieve our creative aims more easily. But what happens when it starts to interfere with the creative process itself? Is AI starting to tell us what to shoot, how to shoot it and what it should look like when it's edited?

Luminar will analyse the content of your photos using AI and then suggest templates (presets) to make them look wonderful. Lightroom offers AI-driven preset suggestions.

It all seems harmless enough, and perhaps ideal for beginners still exploring different visual styles, but it also leads people towards a broad, generic taste suggested by a machine learning algorithm.

Are we perhaps heading for a future where every image is good but the same. A future of universal competence, with visual value determined by an algorithm, coded by people who have never taken a photograph but know how to analyse the heck out of data.

It's easy to fall into this idea there is a universal good selection, good photograph or good styling choice. This is a great way to breed a race of imaging automatons, and to make sure that we never again have a Vivian Maier, Henri Cartier-Bresson, Annie Leibovitz or Bill Brandt.

AI didn't invent photographic fakery, but it's sure made it easier. Personally, I'm fixated on Skylum's AI Sky Replacement. In a few moments you can turn ordinary scenes into something surreal, dramatic or beautiful.

But this is fine. Photography doesn't have to be a forensic record of reality. Some photographers like to record what's there and what's real; others like to make images that evoke ideas or moods.

AI sky replacement, portrait enhancement and masking are great tools for making images of the second kind but, of course there is a crossover. Very often a landscape will look better with a different sky, and a portrait sitter might be much happier with an edited version of themselves.

This is where it gets tricky, especially in the new 'influencer economy'. It's very easy now for anyone to fake a photo but still present it as 'reality' (often because they don't know any better). You can make yourself look beautiful, your travels look amazing and your life look wonderful and all the time your AI-enhanced photos are being offered as evidence of these things. That's a bit naughty.

Are we really so lazy, insecure, fame-hungry and ignorant that we will happily let a machine do our thinking for us? Nobody wants to take a bad picture, but surely thats a risk worth taking compared to the alternative surrendering to an AI-driven universality where your best pictures are the same as everyone elses and you happily trade your individuality for popularity.

It's not all AI's fault. Social media algorithms are also to blame. They've led us to imagine that what's popular is good, and that instant impact is all that matters. It's fine for AI to help us be more individual and creative, but not if all it's doing is reducing photography to a bunch of popular memes.

The history of photography is a delightful mixture of the kitsch and the popular and the wild and individualistic. We need to keep it that way.

See original here:

Artificial intelligence in photography? What happened to the ordinary kind? - Digital Camera World

The Industry 4.0 era of manufacturing depends so heavily on data-driven precision that artificial intelligence (AI) is playing an increasing role in harnessing that data to enhance the performance of machines including injection molders.

AI in manufacturing encompasses an array of technologies that allow machines to perform with intelligence that emulates that of humans. Machine learning and natural language processing help machines approximate the human capacity to learn, make judgments, and solve problems. Data-enhanced efficiency keeps processes moving faster and more cost-effectively.

AI is becoming increasingly important in mechanical engineering, not least because of the need to automate injection molding processes efficiently and flexibly despite ever smaller batch sizes and shorter product life cycles, said Werner Faulhaber, Director of Research and Development at Arburg. Application examples of AI include automatic programming of robotic systems, targeted malfunction remedying, and a spare parts system with intelligent image processing. Arburg is working on making injection molding more intelligent, step by step ensuring that the machine continuously learns, keeps itself stable, and can even optimize itself in the future.

Arburg forms flexible and controllable production systems by combining machines, automation, and proprietary IT solutions. The companys Gestica control system, with its intelligent assistant functions, is integral to those systems. All Kuka six-axis robots, for example, have been equipped with the new Gestica user interface as standard, Faulhaber noted. This simplifies programming, as well as the monitoring, storage, and evaluation of process data.

One application Arburg is working on is the automatic programming of its Multilift linear robotic systems. The idea is that the operator simply enters the destination, as with a car navigation device, and the system automatically calculates the optimal route. For robotic systems, this means that the operator simply enters the desired start and end positions, and the control system takes care of the rest.

Wittmann Battenfeld, which has fully embraced Industry 4.0 connectivity across its portfolio of injection molding and auxiliary machines over the past several years, employs AI with its robots to monitor cycle times and control robots speeds outside the molding machine.

The companys machine-learning capabilities HiQ Flow and CMS technology will be on display at this years K show on Oct. 19 to 26 in Dsseldorf, Germany. The speed of ROI can be as short as a few cycles with HiQ Flow, and the software can often be retrofitted to older injection molding machines equipped with a B8 machine control. A CMS Pro version will be available at a later date.

The technology draws new conclusions from current parameters and, thus, becomes increasingly intelligent as it monitors performance, said Product Manager Christian Glueck. We limit it to a methodical determination of parameters. Therefore, the time required to use the technology is minimal, as is the price.

Comparing AI and machine learning, Glueck said, AI actually requires a much higher time investment and, correspondingly, a higher financial investment. A large number of parameters must be recorded from a running process and the relevant parameters are determined on the basis of the deviations. These are compared with measurement data of the product.

Based on factors like changes in material, ambient temperature, machine wear, tool wear, and other influences, AI can determine which machine parameters need to be changed so that the product can be produced within its quality tolerances. This can take months, as errors first must occur in order to learn from them.

Wittmann co-funded such an assessment program with Austrias Montanuniversitt Leoben university, but we found that the time needed to make it workable for production had to be questioned because in addition to the long-term investigation of the process, you also need the manpower necessary to handle it.

The companys Eco-Mode saves wear and tear on the robot by ensuring it does not run faster than necessary ultimately saving maintenance and energy costs. Offered standard on many Wittmann robots, Eco-Mode requires no special programming or interface with the IMM or operator/programmer, said Jason Long, National Sales Manager for robots and automation for Wittmann USA. All the end user has to do is tell the robot how many seconds it should get back over the IMM before the mold opens.

Another Wittmann feature, Eco-Vac conserves energy by setting a few parameters on the robot and allowing the robot to turn its vacuum circuits off and on. The robot monitors the vacuum level of the circuit used for picking the part out of the mold. If the robot senses the vacuum has reduced to a level that it could drop the part before it is told to, the robot will turn the vacuum on until it reaches the safe level again, then shuts back off. This feature cuts the amount of compressed air each robot uses and could save customers hundreds of dollars a year per robot.

As AI and machine learning are further leveraged to improve injection molding operations, simply gathering data is not enough to optimize processes, Faulhaber cautioned. You also need the process expertise and domain knowledge. In the future, the evaluation of many data directly in the control unit will offer further added value.

Arburg uses AI to develop master models using experience and data collected over the years on process, material, and machinery, Faulhaber continued. The customer could then sharpen the provided master model on edge and optimize their processes. The in-house development Gestica control system, the Arburg host computer system, and the arburgXworld customer portal give an advantage here.

One of Arburg's medium-term goals is to develop a system for digital twins of customized injection molding machines. This will open up completely new possibilities for simulating the cycle and making energy predictions. In addition, 3D views and installation plans of the machine stored in the arburgXworld customer portal and in the control system support the operator, said Faulhaber.

Link:

How Artificial Intelligence Is Transforming Injection Molding - Plastics Today

The use of artificial intelligence (AI) for war has been a promise of science fiction and politicians for years, but new research from the Georgia Institute of Technology claims to show the value that AI can automate only a limited subset of human judgment. "All of the hard problems in AI really are judgment and data problems, and the interesting thing about that is when you start thinking about war, the hard problems are strategy and uncertainty, or what is well known as the fog of war," said Jon Lindsay, an associate professor in the School of Cybersecurity & Privacy and the Sam Nunn School of International Affairs. "You need human sense-making and to make moral, ethical, and intellectual decisions in an incredibly confusing, fraught, scary situation." AI decision-making is based on four key components: data about a situation, interpretation of those data (or prediction), determining the best way to act in line with goals and values (or judgment), and action. Machine learning advancements have made predictions easier, which makes data and judgment even more valuable. Although AI can automate everything from commerce to transit, judgment is where humans must intervene, Lindsay and University of Toronto Professor Avi Goldfarb wrote in the paper, "Prediction and Judgment: Why Artificial Intelligence Increases the Importance of Humans in War," published in International Security.

Many policymakers assume human soldiers could be replaced with automated systems, ideally making militaries less dependent on human labor and more effective on the battlefield. This is called the substitution theory of AI, but Lindsay and Goldfarb state that AI should not be seen as a substitute, but rather as a complement to existing human strategy.

"Machines are good at prediction, but they depend on data and judgment, and the most difficult problems in war are information and strategy," he said. "The conditions that make AI work in commerce are the conditions that are hardest to meet in a military environment because of its unpredictability."

An example Lindsay and Goldfarb highlight is the Rio Tinto mining company, which uses self-driving trucks to transport materials, reducing costs and risks to human drivers. There are abundant, predictable, and unbiased data traffic patterns and maps that require little human intervention unless there are road closures or obstacles.

War, however, usually lacks abundant unbiased data, and judgments about objectives and values are inherently controversial, but that doesn't mean it's impossible. The researchers argue AI would be best employed in bureaucratically stabilized environments on a task-by-task basis.

"All the excitement and the fear are about killer robots and lethal vehicles, but the worst case for military AI in practice is going to be the classically militaristic problems where you're really dependent on creativity and interpretation. But what we should be looking at is personnel systems, administration, logistics, and repairs," Lindsay said.

There are also consequences to using AI for both the military and its adversaries, according to the researchers. If humans are the central element to deciding when to use AI in warfare, then military leadership structure and hierarchies could change based on the person in charge of designing and cleaning data systems and making policy decisions. This also means adversaries will aim to compromise both data and judgment since they would largely affect the trajectory of the war. Competing against AI may push adversaries to manipulate or disrupt data to make sound judgment even harder. In effect, human intervention will be even more necessary.

Yet this is just the start of the argument and innovations.

"If AI is automating prediction, that's making judgment and data really important," Lindsay said. "We've already automated a lot of military action with mechanized forces and precision weapons, then we automated data collection with intelligence satellites and sensors, and now we're automating prediction with AI. So, when are we going to automate judgment, or are there components of judgment cannot be automated?"

Until then, though, tactical and strategic decision-making by humans continues to be the most important aspect of warfare. (ANI)

Go here to read the rest:

An artificial intelligence-based strategy or judgement cannot be trusted by the military, according to researc - Times Now

Dublin, June 14, 2022 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence in Supply Chain Market (2022-2027) by Offering, Technology, Application, Industry, Geography, Competitive Analysis, and the Impact of Covid-19 with Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence in Supply Chain Market is estimated to be USD 3.3 Bn in 2022 and is projected to reach USD 10.49 Bn by 2027, growing at a CAGR of 26.02%.

Market Dynamics

Market dynamics are forces that impact the prices and behaviors of the Global Artificial Intelligence in Supply Chain Market stakeholders. These forces create pricing signals which result from the changes in the supply and demand curves for a given product or service. Forces of Market Dynamics may be related to macro-economic and micro-economic factors. There are dynamic market forces other than price, demand, and supply. Human emotions can also drive decisions, influence the market, and create price signals.

As the market dynamics impact the supply and demand curves, decision-makers aim to determine the best way to use various financial tools to stem various strategies for speeding the growth and reducing the risks.

Company Profiles

The report provides a detailed analysis of the competitors in the market. It covers the financial performance analysis for the publicly listed companies in the market. The report also offers detailed information on the companies' recent development and competitive scenario. Some of the companies covered in this report are CH Robinson, FedEx, Google, Koch Industries, Microsoft, NVIDIA, Oracle, Splice Machine, Xilinx, etc.

Countries Studied

Competitive Quadrant

The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

Ansoff Analysis

The report presents a detailed Ansoff matrix analysis for the Global Artificial Intelligence in Supply Chain Market. Ansoff Matrix, also known as Product/Market Expansion Grid, is a strategic tool used to design strategies for the growth of the company. The matrix can be used to evaluate approaches in four strategies viz. Market Development, Market Penetration, Product Development and Diversification. The matrix is also used for risk analysis to understand the risk involved with each approach.

The report analyses the Global Artificial Intelligence in Supply Chain Market using the Ansoff Matrix to provide the best approaches a company can take to improve its market position.

Based on the SWOT analysis conducted on the industry and industry players, the analyst has devised suitable strategies for market growth.

Why buy this report?

Key Topics Covered:

1 Report Description

2 Research Methodology

3 Executive Summary

4 Market Dynamics4.1 Drivers4.1.1 Increasing Focus on Customer-Centric Marketing Strategies4.1.2 Increasing Use of Digital Network and Social Media for Marketing4.1.3 AI Benefits in Customer Acquisition and Lead Generation4.2 Restraints4.2.1 Lack of AI Professionals4.3 Opportunities4.3.1 Growing Cloud-Based Applications Adoption4.3.2 Increasing Scope in Marketing Analysis4.4 Challenges4.4.1 Slow Digitization Rate Affecting in Emerging Markets

5 Market Analysis5.1 Regulatory Scenario5.2 Porter's Five Forces Analysis5.3 Impact of COVID-195.4 Ansoff Matrix Analysis

6 Global Artificial Intelligence in Supply Chain Market, By Offering6.1 Introduction6.2 Hardware6.2.1 Memory6.2.2 Network6.2.3 Processors6.3 Services6.3.1 Deployment & Integration6.3.2 Support & Maintenance6.4 Software

7 Global Artificial Intelligence in Supply Chain Market, By Technology7.1 Introduction7.2 Computer Vision7.3 Context-Aware Computing7.4 Machine Learning7.4.1 Reinforcement Learning7.4.2 Supervised Learning7.4.3 Unsupervised Learning7.5 Natural Language Processing (NLP)

8 Global Artificial Intelligence in Supply Chain Market, By Application8.1 Introduction8.2 Fleet Management8.3 Freight Brokerage8.4 Risk Management8.5 Supply Chain Planning8.6 Virtual Assistant8.7 Warehouse Management

9 Global Artificial Intelligence in Supply Chain Market, By Industry9.1 Introduction9.2 Aerospace9.3 Automotive9.4 Consumer-Packaged Goods9.5 Food & Beverages9.6 Healthcare9.7 Manufacturing9.8 Retail

10 Americas' Artificial Intelligence in Supply Chain Market10.1 Introduction10.2 Argentina10.3 Brazil10.4 Canada10.5 Chile10.6 Colombia10.7 Mexico10.8 Peru10.9 United States10.10 Rest of Americas

11 Europe's Artificial Intelligence in Supply Chain Market11.1 Introduction11.2 Austria11.3 Belgium11.4 Denmark11.5 Finland11.6 France11.7 Germany11.8 Italy11.9 Netherlands11.10 Norway11.11 Poland11.12 Russia11.13 Spain11.14 Sweden11.15 Switzerland11.16 United Kingdom11.17 Rest of Europe

12 Middle East and Africa's Artificial Intelligence in Supply Chain Market12.1 Introduction12.2 Egypt12.3 Israel12.4 Qatar12.5 Saudi Arabia12.6 South Africa12.7 United Arab Emirates12.8 Rest of MEA

13 APAC's Artificial Intelligence in Supply Chain Market13.1 Introduction13.2 Australia13.3 Bangladesh13.4 China13.5 India13.6 Indonesia13.7 Japan13.8 Malaysia13.9 Philippines13.10 Singapore13.11 South Korea13.12 Sri Lanka13.13 Thailand13.14 Taiwan13.15 Rest of Asia-Pacific

14 Competitive Landscape14.1 Competitive Quadrant14.2 Market Share Analysis14.3 Strategic Initiatives14.3.1 M&A and Investments14.3.2 Partnerships and Collaborations14.3.3 Product Developments and Improvements

15 Company Profiles 15.1 Alibaba Group Holding15.2 Amazoncom15.3 American Software15.4 CH Robinson15.5 C315.6 Deutsche Post15.7 E2open15.8 Echo GlobalLogistics15.9 FedEx 15.10 Google 15.11 HAVI Group15.12 Intel 15.13 IBM15.14 Koch Industries 15.15 Microsoft 15.16 NVIDIA15.17 Oracle 15.18 Project4415.19 Relex Solution15.20 Samsung Electronics15.21 SAP America15.22 Splice Machine15.23 TTEC Holdings15.24 Xilinx

16 Appendix

For more information about this report visit https://www.researchandmarkets.com/r/jiguzx

See more here:

The Worldwide Artificial Intelligence in Supply Chain Industry is Expected to Reach $10 Billion by 2027 - GlobeNewswire

Using AI to combine patient data with results from a test for levels of a protein made by the heart could help doctors spot heart failure sooner and improve patient care, experts say.

Acute heart failure affects nearly one million people in the UK and accounts for five percent of all unplanned hospital admissions, writes the University of Edinburgh in a press release. It is a life-threatening condition caused when the heart is suddenly unable to pump blood around the body.

Diagnosis is difficult because symptoms, such as shortness of breath and leg swelling, occur in many other illnesses. Previous research has shown that patients who are diagnosed quickly benefit the most from treatment.

Researchers from the University of Edinburgh and 13 other countries combined data from 10,369 patients with suspected acute heart failure to develop a tool called CoDE-HF to inform clinicians decisions.

CoDE-HF uses AI to combine routinely collected patient information with results from a blood test for the heart protein NT-proBNP to produce an estimate of whether they suffered heart failure.

The current recommended diagnosis method is to test to see if levels of NT-proBNP are below a certain cut-off value, but this is not widely used as levels can vary depending on an individuals age, weight and other health conditions.

As well as spotting acute heart failure more accurately than heart protein blood tests on their own, CoDE-HF was especially precise in difficult to diagnose patient groups such as older people and those with pre-existing medical conditions.

Dr Ken Lee, cardiology specialist registrar and clinical lecturer at the University of Edinburgh said: Heart failure can be a very challenging diagnosis to make in practice. We have shown that CoDE-HF, our decision-support tool, can substantially improve the accuracy of diagnosing heart failure compared to current blood tests.

AI system predicts risk of heart attack based on retinal scan

Is the eye a window into heart disease? An international team of researchers has developed an AI system that can identify patients who are likely to have a heart attack over the next year.

The team are currently conducting further studies to understand how this decision-support tool will work in the hospital environment and influence patient outcomes.

The research was funded by the British Heart Foundation and the findings have been published in The BMJ.

Mr Dimitrios Doudesis, research fellow and data scientist at the University of Edinburgh said: Our study demonstrates that the application of artificial intelligence in healthcare has major potential to help doctors deliver more personalized patient care.

Professor Nicholas Mills, British Heart Foundation Professor of Cardiology at the University of Edinburgh and Consultant Cardiologist said: The application of artificial intelligence in decision-support tools as CoDE-HF to deliver more personalized care is particularly important given our ageing patient population who are living longer with more pre-existing medical conditions. We are currently conducting further studies to identify ways to implement CoDE-HF effectively in routine care.

See more here:

Artificial Intelligence can spot heart failure better than current tests - Innovation Origins

Throughout history, technology has never advanced as quickly as it is right now. It's becoming harder than ever for investors to track the sheer number of innovative tech companies in the public markets, each offering its own unique vision for the future.

But perhaps no technology is more transformative than artificial intelligence (AI), which is already being deployed to complete highly complex tasks in a fraction of the time that humans can. According to one estimate, up to 70% of companies worldwide will have integrated some form of AI into their businesses by 2030, adding $13 trillion in additional output to the global economy.

There will be no shortage of opportunities in the sector over the next decade, but these two stocks might be a great place to start given they're trading at hefty discounts to their all-time highs amid the broader tech sell-off.

C3ai (AI 4.56%) is a first-of-its-kind enterprise AI company. Its stock is volatile because the company isn't profitable yet, and its revenue growth has underperformed expectations since it was listed on the public markets in December 2020. But that's often part and parcel of breaking ground in a brand-new industry.

C3.ai is a good place to start for investors who want exposure to the artificial intelligence sector because it builds both ready-made and customizable AI applications for 11 different industries. For most of its customers, C3.ai is the one-stop source for their AI needs, and it's possible they wouldn't otherwise have access to the technology.

The oil and gas industry is C3.ai's largest contributor to revenue, making up 54% of its $252 million in fiscal 2022, which ended on April 30. The company's technology helps oil behemoths like Shelltrack thousands of pieces of equipment to predict potentially catastrophic failures, saving time, money, and negative environmental impacts. C3.ai has an entire suite of applications just for the fossil fuel sector, which also helps those companies manage carbon emissions to run cleaner businesses.

But C3.ai has also drawn recognition from the largest tech companies in the world. It has partnerships with both Microsoftand Alphabet'sGoogle to collaboratively develop AI applications to better serve their customers using cloud computing technology.

C3.ai's stock price is down 88% from its all-time high, so it carries inherent risks. The company lost $192 million in fiscal 2022 (which ended April 30), but importantly, it has $959 million in cash, equivalents, and short-term investments on its balance sheet -- which means it can run at that loss rate for the next five years before it needs more money. C3.ai has a high gross profit margin of 81%, so once it achieves scale, it can cut back its operating costs to generate positive earnings. But the key question is how long it will take to get there, if at all. With new businesses in new industries, it's always an unknown.

But C3.ai estimates its AI software opportunity could be worth $596 billion by 2025. Since the company's market value is only $2 billion now, it might be worth a small bet for investors with some risk appetite.

Upstart Holdings (UPST 2.10%) offers a great example of how artificial intelligence is being used to improve decades-old processes. Its AI-powered algorithm is designed to replace Fair Isaac's FICO credit scoring system, which is the traditional means of assessing a potential borrower's creditworthiness. Upstart can analyze as many as 1,600 data points about an applicant to deliver a loan decision almost instantly 74% of the time, a feat that might take human assessors days or even weeks to determine.

Fifty-seven banks and credit unions have signed on to use Upstart's algorithm, and one bank, in particular, has abandoned FICO scores altogether in its favor. This is key because Upstart isn't a lender; it originates loans for its bank partners in exchange for a fee. But the company was forced to deviate from this strategy slightly in the recent first quarter of 2022 amid turbulent credit market conditions. Upstart absorbed $345 million worth of new loans using its own balance sheet, which spooked investors.

This added to the $252 million worth of loans it already held mostly for research and development purposes. Management says the increase is only temporary, and it's important to note the $345 million jump represented just 7% of the total $4.5 billion in originations during the quarter.

It's partly a symptom of Upstart's rapid growth, which is bolstered by its entry into the automotive loan origination space. Since launching its car sales and origination software in 2021 called Upstart Auto Retail, 35 car makers have adopted the platform across 525 dealerships. That's up 224% from 162 dealers in Q1 last year.

Upstart generated $849 million in revenue during 2021, a whopping 264% year-over-year jump. It thinks revenue could top $1.25 billion this year, and while that's a slowdown in growth, consumers are contending with higher interest rates and tougher economic conditions, which could dampen demand for credit.

But the company continues to expand into what it estimates is a $6 trillion addressable opportunity. With its stock price down 90% from its all-time high, it might be a great chance to make a long-term bet on what could be the future of credit assessments.

More here:

2 Artificial Intelligence Growth Stocks to Buy on the Dip - The Motley Fool

To many they are arts next big thing digital images of jellyfish pulsing and blurring in a dark pink sea, or dozens of butterflies fusing together into a single organism.

The Argentine artist Sofia Crespo, who created the works with the help of artificial intelligence, is part of the generative art movement, where humans create rules for computers which then use algorithms to generate new forms, ideas and patterns.

The field has begun to attract huge interest among art collectors and even bigger price tags at auction.

US artist and programmer Robbie Barrat a prodigy still only 22 years old sold a work called Nude Portrait#7Frame#64 at Sothebys in March for 630,000 ($821,000).

That came almost four years after French collective Obvious sold a work at Christies titled Edmond de Belamy largely based on Barrats code for $432,500.

Collector Jason Bailey told AFP that generative art was like a ballet between humans and machines.But the nascent scene could already be on the verge of a major shake-up, as tech companies begin to release AI tools that can whip up photo-realistic images in seconds.

Artists in Germany and the United States blazed a trail in computer-generated art during the 1960s.

The V&A museum in London keeps a collection going back more than half a century, one of the key works being a 1968 piece by German artist Georg Nees called Plastik 1.

Nees used a random number generator to create a geometric design for his sculpture.

Nowadays, digital artists work with supercomputers and systems known as Generative Adversarial Networks (GANs) to create images far more complex than anything Nees could have dreamed of.

GANs are sets of competing AIs - one generates an image from the instructions it is given, the other acts as a gatekeeper, judging whether the output is accurate.

If it finds fault, it sends the image back for tweaks and the first AI gets back to work for a second try to beat the gamekeeper.But artists like Crespo and Barrat insist that the artist is still central to the process, even if their working methods are not traditional.

When Im working this way, Im not creating an image. Im creating a system that can create images, Barrat told AFP.

Crespo said she thought her AI machine would be a true collaborator, but in reality it is incredibly tough to get even a single line of code to generate satisfactory results.

She said it was more like babysitting the machine. Tech companies are now hoping to bring a slice of this rarefied action to regular consumers.

Google and Open AI are both touting the merits of new tools they say bring photorealism and creativity without the need for coding skills.

They have replaced GANs with more user-friendly AI models called transformers that are adept at converting everyday speech into images.

Google Imagens webpage is filled with absurdist images generated by instructions such as: A small cactus wearing a straw hat and neon sunglasses in the Sahara desert.

Open AI boasts that its Dalle-2 tool can offer any scenario in any artistic style from the Flemish masters to Andy Warhol.

Although the arrival of AI has led to fears of humans being replaced by machines in fields from customer care to journalism, artists see the developments more as an opportunity than a threat.

Crespo has tried out Dalle-2 and said it was a new level in terms of image generation in general though she prefers her GANs. I very often dont need a model that is very accurate to generate my work, as I like very much when things look indeterminate and not easily recognizable, she said.

Camille Lenglois of Pariss Pompidou Centre Europes largest collection of contemporary art also played down any idea that artists were about to be replaced by machines.

She told AFP that machines did not yet have the critical and innovative capacity, adding: The ability to generate realistic images does not make one an artist.

Here is the original post:

Is Artificial Intelligence the future of art? : - The Tico Times

Background: Patients with retinal diseases may exhibit serious complications that cause severe visual impairment owing to a lack of awareness of retinal diseases and limited medical resources. Understanding how artificial intelligence (AI) is used to make predictions and perform relevant analyses is a very active area of research on retinal diseases. In this study, the relevant Science Citation Index (SCI) literature on the AI of retinal diseases published from 2012 to 2021 was integrated and analyzed.

Objective: The aim of this study was to gain insights into the overall application of AI technology to the research of retinal diseases from set time and space dimensions.

Methods: Citation data downloaded from the Web of Science Core Collection database for AI in retinal disease publications from January 1, 2012, to December 31, 2021, were considered for this analysis. Information retrieval was analyzed using the online analysis platforms of literature metrology: Bibliometrc, CiteSpace V, and VOSviewer.

Results: A total of 197 institutions from 86 countries contributed to relevant publications; China had the largest number and researchers from University College London had the highest H-index. The reference clusters of SCI papers were clustered into 12 categories. Deep learning was the cluster with the widest range of cocited references. The burst keywords represented the research frontiers in 2018-2021, which were eye disease and enhancement.

Conclusions: This study provides a systematic analysis method on the literature regarding AI in retinal diseases. Bibliometric analysis enabled obtaining results that were objective and comprehensive. In the future, high-quality retinal imageforming AI technology with strong stability and clinical applicability will continue to be encouraged.

Read more from the original source:

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study - Newswise