Technavio has been monitoring the artificial intelligence (AI) market in manufacturing industry and it is poised to grow by USD 7.22 billion during 2019-2023, progressing at a CAGR of about 31% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20200429005020/en/

Technavio has announced its latest market research report titled Global Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023 (Graphic: Business Wire)

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Please Request Latest Free Sample Report on COVID-19 Impact

The market is fragmented, and the degree of fragmentation will accelerate during the forecast period. Amazon Web Services Inc., FANUC Corp., General Electric Co., Google LLC, H2O.AI Inc., IBM Corp., KUKA Aktiengesellschaft, Microsoft Corp., Rockwell Automation Inc., and SAP SE are some of the major market participants. The use of industrial IoT will offer immense growth opportunities. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Use of industrial IoT has been instrumental in driving the growth of the market.

Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023: Segmentation

Artificial Intelligence (AI) Market in Manufacturing Industry is segmented as below:

To learn more about the global trends impacting the future of market research, download latest free sample report of 2020-2024: https://www.technavio.com/talk-to-us?report=IRTNTR32119

Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023: Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. Our artificial intelligence (AI) market in manufacturing industry report covers the following areas:

This study identifies increasing human-robot collaboration as one of the prime reasons driving the artificial intelligence (AI) market growth in the manufacturing industry during the next few years.

Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023: Vendor Analysis

We provide a detailed analysis of vendors operating in the artificial intelligence (AI) market in manufacturing industry, including some of the vendors such as Amazon Web Services Inc., FANUC Corp., General Electric Co., Google LLC, H2O.AI Inc., IBM Corp., KUKA Aktiengesellschaft, Microsoft Corp., Rockwell Automation Inc., and SAP SE. Backed with competitive intelligence and benchmarking, our research reports on the artificial intelligence (AI) market in manufacturing industry are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.

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Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023: Key Highlights

Table Of Contents:

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PART 01: EXECUTIVE SUMMARY

PART 02: SCOPE OF THE REPORT

PART 03: MARKET LANDSCAPE

PART 04: MARKET SIZING

PART 05: FIVE FORCES ANALYSIS

PART 06: MARKET SEGMENTATION BY APPLICATION

PART 07: CUSTOMER LANDSCAPE

PART 08: GEOGRAPHIC LANDSCAPE

PART 09: DRIVERS AND CHALLENGES

PART 10: MARKET TRENDS

PART 11: VENDOR LANDSCAPE

PART 12: VENDOR ANALYSIS

PART 13: APPENDIX

PART 14: EXPLORE TECHNAVIO

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200429005020/en/

Contacts

Technavio ResearchJesse MaidaMedia & Marketing ExecutiveUS: +1 844 364 1100UK: +44 203 893 3200Email: media@technavio.com Website: http://www.technavio.com/

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Analysis on Impact of Covid-19- Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023 | Use of Industrial IoT to Boost Growth |...

Abrigo, a leading provider of compliance, credit risk, lending, and asset/liability management solutions, announced upgrades to BAM+, its financial crimes detection software, including the addition of five transparent artificial intelligence/machine learning (AI/ML) anti-money laundering (AML) scenarios and the ability to direct file Suspicious Activity Report (SAR) and Currency Transaction Report (CTR) batches to FinCEN.

CISOs Believe Dedicated Cyber Security Investment Is Still Not Prioritized

Abrigo developed each enhancement to enable users to work more efficiently while handling fewer false positives so they can focus on truly suspicious activity. With the new upgrade, users will have access to:

On a recent customer webinar, over 80% of attendees identified direct file with FinCEN, machine learning scenarios, and new structuring scenarios as the upgrades they were most excited about.

By enabling direct file of both CTRs and SARs to FinCEN, users can batch upload these critical reports and receive FinCEN acknowledgment of the upload directly within the platform. This process saves time and ensures that nothing falls through the cracks while alerting to potentially suspicious activity.

The newAI/ML scenariosdetermine typical patterns in behavior and alert when a transaction is different from that normal account behavior. All five scenarios are based on the same anomaly detection algorithm designed in-house by Abrigos product team using the Microsoft ml.net framework. The models are powered by transparent machine learning models that easily allow the end-user to explain to regulators how they work, a key component to any AML technology.

The five scenarios monitor against credit transactions, other credit or debit transactions, remote deposit capture, and daily ACH debit. Similar to other scenarios within BAM+, users can institute specific amount thresholds with each scenario and apply them to user-defined groups.

Rationalizing Cyber Security Solutions

Abrigo is thrilled to release our ML-based scenarios to our valued customers and partners, saidDave McCann, Abrigos Chief Technology Officer. We believe that the introduction of machine learning and data tools allow our end-users to increase productivity and the accuracy of the information they work with every day. Our transparent approach to applying these technologies furthers our commitment to being a trusted partner for community financial intuitions.

The new structuring scenarios focus on grouped daily and weekly structuring that allows users to gain quick and easy-to-understand insight into totals for cash in and cash out in one location. This helps provide more accurate alerts and fewer false hits.

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Abrigo Adds Transparent Artificial Intelligence Scenarios, Direct File to FinCEN to its Financial Crime Prevention Software - EnterpriseTalk

In order to marks another step toward bringing the benefits of AI to the digital transformation of the agreement process, DocuSign, which offers eSignature solution as part of the DocuSign Agreement Cloud, has announced the closing of its acquisition of Seal Software, one of the leading contract analytics and artificial intelligence (AI) technology providers.

Seals technology and value proposition can now be more comprehensively integrated across the DocuSign Agreement Cloudthe companys suite of applications and integrations for automating and connecting the entire agreement process. By adding Seal to our growing portfolio, we are enhancing the agreement process through the use of AI-driven analytics and machine learning technology, said Scott Olrich, DocuSigns COO.

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In a blog post, DocuSign revealed the acquisition of Seal allows the company to deliver more AI and analytics capabilities, both now and in the future. In addition to continuing to sell, service, and enhance Intelligent Insights, DocuSign accelerating the development of Seals technology and its integration with other Agreement Cloud products, such as DocuSign CLM.

Besides this, DocuSign is providing a broader range of purpose-built AI models for needs like data privacy, Brexit, LIBOR, and analyzing agreements for COVID-19-related risks such as force majeure clauses, stated the company.

Asserting that the company can serve your analytics needs more broadly and deeply with the infusion of talent that Seal has brought, DocuSign notified, We are advancing our AI analytics infrastructure, which originated from DocuSigns 2017 acquisition of technology from machine learning startup Appuri.

Moreover, DocuSign has also mobilized to accelerate critical agreements needed for healthcare, emergency government services, education, small business lending, work from home, and doing business remotely. The eSignature company has thousands of DocuSigners supporting its customers COVID-19 needs around the world: sales representatives acting as inbound agents for How can we requests, customer-success professionals advising on use-case implementations, product and operations personnel reconfiguring infrastructure to handle unique demand patterns, and marketing team members collecting and communicating what everyone is doing so the learnings are shared.

As far as work from home is concerned, DocuSign is helping organizations expand eSignature availability so people can conduct business from home, as well as helping HR and IT support employees at-home work. In response to work-from-home needs, weve been supporting wider rollouts of eSignature within organizations, allowing more at-home workers to remotely conduct business that requires signing agreements, notified DocuSign.

For the latestgadget and tech news, andgadget reviews, follow us onTwitter,FacebookandInstagram.For newesttech & gadget videossubscribe to ourYouTube Channel. You can also stay up to date using theGadget Bridge Android App.

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DocuSign completes acquisition of Seal Software to deliver benefits of Artificial Intelligence - Gadget Bridge

Intelligence and espionage services need to embrace artificial intelligence (AI) in order to protect national security as cyber criminals and hostile nation states increasingly look to use the technology to launch attacks.

The UK's intelligence and security agency GCHQ commissioned a study into the use of AI for national security purposes. It warns that while the emergence of AI create new opportunities for boosting national security and keeping members of the public safe, it also presents potential new challenges, including the risk of the same technology being deployed by attackers.

"Malicious actors will undoubtedly seek to use AI to attack the UK, and it is likely that the most capable hostile state actors, which are not bound by an equivalent legal framework, are developing or have developed offensive AI-enabled capabilities," says the report from the Royal United Services Institute for Defence and Security Studies (RUSI).

SEE:Cybersecurity: Let's get tactical(ZDNet/TechRepublic special feature) |Download the free PDF version(TechRepublic)

"In time, other threat actors, including cyber-criminal groups, will also be able to take advantage of these same AI innovations."

The paper also warns that the use of AI in the intelligence services could also "give rise to additional privacy and human rights considerations" when it comes to collecting, processing and using personal data to help prevent security incidents ranging from cyberattacks to terrorism.

The research outlines three key areas where intelligence could benefit from deploying AI to help collect and use data for more efficiency.

They are the automation of organisational processes, including data management, as well as the use of AI for cybersecurity in order to identify abnormal network behaviour and malware, and responding to suspected incidents in real time.

The paper also suggests that AI can also aid intelligence analysis and that by using augmented intelligence, algorithms could support a range of human analysis processes.

However, RUSI also points out that artificial intelligence isn't ever going to be a replacement for agents and other personnel.

"None of the AI use cases identified in the research could replace human judgement. Systems that attempt to 'predict' human behaviour at the individual level are likely to be of limited value for threat assessment purposes," says the paper.

SEE: Cybersecurity: Do these ten things to keep your networks secure from hackers

The report does note that deploying AI to boost the capabilities of spy agencies could also lead to new privacy concerns, such as the amount of information being collected around individuals and when cases of suspect behaviour become active investigations and finding the line between the two.

Ongoing cases against bulk surveillance could indicate the challenges the use of AI could face and existing guidance on procedure may need changes to meet the challenges of using AI in intelligence.

Nonetheless, the report argues that despite some potential challenges, AI has the potential to "enhance many aspects of intelligence work".

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Artificial intelligence will be used to power cyberattacks, warn security experts - ZDNet

Ten years ago, if you mentioned the term artificial intelligence in a boardroom theres a good chance you would have been laughed at. For most people it would bring to mind sentient, sci-fi machines such as 2001: A Space Odysseys HAL or Star Treks Data.

Today it is one of the hottest buzzwords in business and industry. AI technology is a crucial lynchpin of much of the digital transformation taking place today as organizations position themselves to capitalize on the ever-growing amount of data being generated and collected.

So how has this change come about? Well partly it is due to the Big Data revolution itself. The glut of data has led to intensified research into ways it can be processed, analyzed and acted upon. Machines being far better suited than humans tothis work, the focus was on training machines to do this in as smart a way as is possible.

This increased interest in research in the field in academia, industry and among the open source community which sits in the middle has led to breakthroughs and advances that are showing their potential to generate tremendous change. From healthcare to self-driving cars to predicting the outcome of legal cases, no one is laughing now!

What is Artificial Intelligence?

The concept of what defines AI has changed over time, but at the core there has always been the idea of building machines which are capable of thinking like humans.

After all, human beings have proven uniquely capable of interpreting the world around us and using the information we pick up to effect change. If we want to build machines to help us do this more efficiently, then it makes sense to use ourselves as a blueprint.

AI, then, can be thought of as simulating the capacity for abstract, creative, deductive thought and particularly the ability to learn which this gives rise to using the digital, binary logic of computers.

Research and development work in AI is split between two branches. One is labelled applied AI which uses these principles of simulating human thought to carry out one specific task. The other is known as generalized AI which seeks to develop machine intelligences that can turn their hands to any task, much like a person.

Research into applied, specialized AI is already providing breakthroughs in fields of study from quantum physics where it is used to model and predict the behavior of systems comprised of billions of subatomic particles, to medicine where it being used to diagnose patients based on genomic data.

In industry, it is employed in the financial world for uses ranging from fraud detection to improving customer service by predicting what services customers will need. In manufacturing it is used to manage workforces and production processes as well as for predicting faults before they occur, therefore enabling predictive maintenance.

In the consumer world more and more of the technology we are adopting into our everyday lives is becoming powered by AI from smartphone assistants like Apples Siri and Googles Google Assistant, to self-driving and autonomous cars which many are predicting will outnumber manually driven cars within our lifetimes.

Generalized AI is a bit futher off to carry out a complete simulation of the human brain would require both a more complete understanding of the organ than we currently have, and more computing power than is commonly available to researchers. But that may not be the case for long, given the speed with which computer technology is evolving. A new generation of computer chip technology known as neuromorphic processors are being designed to more efficiently run brain-simulator code. And systems such as IBMs Watson cognitive computing platform use high-level simulations of human neurological processes to carry out an ever-growing range of tasks without being specifically taught how to do them.

What are the key developments in AI?

All of these advances have been made possible due to the focus on imitating human thought processes. The field of research which has been most fruitful in recent years is what has become known as machine learning. In fact, its become so integral to contemporary AI that the terms artificial intelligence and machine learning are sometimes used interchangeably.

However, this is an imprecise use of language, and the best way to think of it is that machine learning represents the current state-of-the-art in the wider field of AI. The foundation of machine learning is that rather than have to be taught to do everything step by step, machines, if they can be programmed to think like us, can learn to work by observing, classifying and learning from its mistakes, just like we do.

The application of neuroscience to IT system architecture has led to the development of artificial neural networks and although work in this field has evolved over the last half century it is only recently that computers with adequate power have been available to make the task a day-to-day reality for anyone except those with access to the most expensive, specialized tools.

Perhaps the single biggest enabling factor has been the explosion of data which has been unleashed since mainstream society merged itself with the digital world. This availability of data from things we share on social media to machine data generated by connected industrial machinery means computers now have a universe of information available to them, to help them learn more efficiently and make better decisions.

What is the future of AI?

That depends on who you ask, and the answer will vary wildly!

Real fears that development of intelligence which equals or exceeds our own, but has the capacity to work at far higher speeds, could have negative implications for the future of humanity have been voiced, and not just by apocalyptic sci-fi such as The Matrix or The Terminator, but respected scientists like Stephen Hawking.

Even if robots dont eradicate us or turn us into living batteries, a less dramatic but still nightmarish scenario is that automation of labour (mental as well as physical) will lead to profound societal change perhaps for the better, or perhaps for the worse.

This understandable concern has led to the foundation last year, by a number of tech giants including Google, IBM, Microsoft, Facebook and Amazon, of the Partnership in AI. This group will research and advocate for ethical implementations of AI, and to set guidelines for future research and deployment of robots and AI.

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The Complete Beginners' Guide to Artificial Intelligence

Artificial intelligence (AI) technology that uses algorithms to assist in decision-making offers tremendous opportunity to make predictions and evaluate big data. The Federal Trade Commission (FTC), on April 8, 2020, provided reminders in its Tips and Advice blog post,Using Artificial Intelligence and Algorithms.

This is not the first time the FTC has focused on data analytics. In 2016, it issued a Big Data Report. Seehere.

AI technology may appear objective and unbiased, but the FTC warns of the potential for unfair or discriminatory outcomes or the perpetuation of existing socioeconomic disparities. For example, the FTC pointed out, a well-intentioned algorithm may be used for a positive decision, but the outcome may unintentionally disproportionately affect a particular minority group.

The FTC does not want consumers to be misled. It provided the following example: If a companys use of doppelgngers whether a fake dating profile, phony follower, deepfakes, or an AI chatbot misleads consumers, that company could face an FTC enforcement action.

Businesses obtaining AI data from a third-party consumer reporting agency (CRA) and making decisions on that have particular obligations under state and federal Fair Credit Reporting Act (FCRA) laws. Under FCRA, a vendor that assembles consumer information to automate decision-making about eligibility for credit, employment, insurance, housing, or similar benefits and transactions may be a consumer reporting agency. An employer relying on automated decisions based on information from a third-party vendor is the user of that information. As the user, the business must provide consumers an adverse action notice required by FCRA if it takes an adverse action against the consumer. The content of the notice must be appropriate to the adverse action, and may consist of a copy of the consumer report containing AI information, the federal summary of rights, and other information. The vendor that is the CRA has an obligation to implement reasonable procedures to ensure the maximum possible accuracy of consumer reports and provide consumers with access to their own information, along with the ability to correct any errors. The FTC is seeking transparency and the ability to provide well-explained AI decision-making if the consumer asks.

Takeaways for Employers

Carefully review use of AI to ensure it doesnotresult in discrimination. According to the FTC, for credit purposes, use of an algorithm such as a zip code could result in a disparate impact on a particular protected group.

Accuracy and integrity of data is key.

Validation of AI models is important to minimizing risk. Post-validation monitoring and periodic re-validation is important as well.

Review whether federal and state FCRA laws apply.

Continue self-monitoring by asking:

How representative is your data set?

Does your data model account for biases?

How accurate are your predictions based on big data?

Does your reliance on big data raise ethical or fairness concerns?

The FTCs message: use AI, but proceed with accountability and integrity.

Jackson Lewis P.C. 2020

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FTCs Tips on Using Artificial Intelligence (AI) and Algorithms - The National Law Review

For more than seven decades, Artificial Intelligence (AI) has been the talking point of a technological revolution. As stated by John McCarthy, the father of Artificial Intelligence, Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. In simpler terms, AI is the ability of a digital machine to make decisions and perform tasks associated with humans. AI deals with analyzing how a human brain thinks and how it learns, decides, and acts in a situation.

Artificial Intelligence (AI) presents never-ending opportunities to revolutionize technology in every industrial sector, and the automobile industry is not untouched by AI. For example, the autonomous or self-driven car is the hotspot in the latest research, and every car manufacturer is investing heavily in it. IHS Automotive predicts that by the end of 2020, there will be more than 150 million AI-powered cars. Before discussing the application areas of AI in cars and their accessories, lets highlight the benefits AI offers in the automobile sector:

Car manufacturers are already using several AI features like voice-control, lane-switch, collision-detection, etc. to improve driver safety. As technology evolves, car accessories like video cameras, sensors, etc. are using AI to provide maximum comfort to the drivers. Lets take a look at how AI is improving the car Industry:

Before we adapt to fully-autonomous cars, it makes sense to evaluate the capabilities of AI by incorporating driver-assist features. AI uses several sensors for blind-spot monitoring, collision detection, pedestrian detection, lane monitoring, etc. to identify dangerous situations and alert the driver accordingly. Similarly, AI-based algorithms can analyze the data from vibration sensors to detect anomalies. Moreover, with new technology coming up, you could determine the load theroof rackis carrying which can help prevent overloading.

With AI, the concept of maintenance shifts from preventive to predictive one. Rather than depending on the event-driven or time-driven approaches for scheduling the maintenance, AI can help in providing actionable insights for your car maintenance. In addition to the historical data, AI uses sensors and contextual data like geographic or weather details. By analyzing the data and through machine learning, AI can offer alerts for real-time condition-based maintenance requirements for your car.

According to the history of the driver, AI can predict the issues resulting from his absent-mindedness. By analyzing the driving pattern, AI can predict the risk that might arise from the drivers personal life or professional life. Similarly, by using fatigue monitoring devices, AI can monitor the vitals of the driver to alert him and take control of the vehicle in case of an emergency. An AI-driven camera can track drowsiness in the driver and trigger an alarm.

With AI, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is possible. With such technology, your car can communicate with other vehicles, as well as the road signs, traffic signals, etc. By enabling vehicles to communicate with each other, you can seamlessly enjoy advanced features like lane monitoring, lane switching, cruise control, etc. Similarly, V2I communication allows you to re-route your vehicle to avoid congested roads. In a nutshell, enhanced communication reduces the chances of accidents and takes you to your destination with less hassle.

The insurance sector deals with managing data from several fields, and AI offers immense potential for improvement. For example, an in-car camera can record accidents that might be helpful during legal or insurance settlements. Similarly, AI can quickly process the data and make the claim-settlement process faster. Using the data analyzing properties of AI, one can even prepare profiles of drivers and check the fraudulent claims.

Apart from elevating the driving experience, AI can transform the way we build cars as well. For over five decades, machines have helped in the assembly lines of the vehicle manufacturers. However, by using AI, we can develop smart robots that work alongside their human counterparts rather than working for them. For example, AI helps in designing autonomous delivery vehicles to transport components in aplant. Similarly, smart, wearable robots work collaboratively with workers to offer up to 20% increase in production efficiency.

AI in the automobile sector promises to change the way we drive cars. The benefits of the AI car accessories are already visible, and its potential is endless. The rewards and opportunities of AI in elevating the overall safety and driving experience attract huge interest by tech-giants as well as startups.

The application areas mentioned above give you a flavor of the AI in the car accessories market. From making the car safer to predicting the maintenance, from easing the insurance claim process to providing hi-tech features, AI caters to the all-round improvement in the driving quality.

https://www.linkedin.com/pulse/how-artificial-intelligence-machine-learning-auto-models-mishanin

https://www.t3.com/features/5-car-innovations-that-are-right-around-the-corner

https://hackernoon.com/what-is-the-role-of-ai-in-future-cars-52c6632ec6cd

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How Artificial Intelligence is Changing the Auto Industry - Legal Examiner

Developing drugs to combat Covid-19 is a global priority, requiring communities to come together to fight the spread of infection. At MIT, researchers with backgrounds in machine learning and life sciences are collaborating, sharing datasets and tools to develop machine learning methods that can identify novel cures for Covid-19.

This research is an extension of a community effort launched earlier this year. In February, before the Institute de-densified as a result of the pandemic, the first-ever AI Powered Drug Discovery and Manufacturing Conference, conceived and hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health, drew attendees including pharmaceutical industry researchers, government regulators, venture capitalists, and pioneering drug researchers. More than 180 health care companies and 29 universities developing new artificial intelligence methods used in pharmaceuticals got involved, making the conference a singular event designed to lift the mask and reveal what goes on in the process of drug discovery.

As secretive as Silicon Valley seems, computer science and engineering students typically know what a job looks like when aspiring to join companies like Facebook or Tesla. But the global head of research and development for Janssen the innovative pharmaceutical company owned by Johnson & Johnson said its often much harder for students to grasp how their work fits into drug discovery.

Thats a problem at the moment, Mathai Mammen says, after addressing attendees, including MIT graduate students and postdocs, who gathered in the Samberg Conference Center in part to get a glimpse behind the scenes of companies currently working on bold ideas blending artificial intelligence with health care. Mathai, who is a graduate of the Harvard-MIT Program in Health Sciences and Technology and whose work at Theravance has brought to market five new medicines and many more on their way, is here to be part of the answer to that problem. What the industry needs to do, is talk to students and postdocs about the sorts of interesting scientific and medical problems whose solutions can directly and profoundly benefit the health of people everywhere he says.

The conference brought together research communities that rarely overlap at technical conferences, says Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, Jameel Clinic faculty co-lead, and one of the conference organizers. This blend enables us to better understand open problems and opportunities in the intersection. The exciting piece for MIT students, especially for computer science and engineering students, is to see where the industry is moving and to understand how they can contribute to this changing industry, which will happen when they graduate.

Over two days, conference attendees snapped photographs through a packed schedule of research presentations, technical sessions, and expert panels, covering everything from discovering new therapeutic molecules with machine learning to funding AI research. Carefully curated, the conference provided a roadmap of bold tech ideas at work in health care now and traced the path to show how those tech solutions get implemented.

At the conference, Barzilay and Jim Collins, the Termeer Professor of Medical Engineering and Science in MITs Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, and Jameel Clinic faculty co-lead, presented research from a study published in Cell where they used machine learning to help identify a new drug that can target antibiotic-resistant bacteria. Together with MIT researchers Tommi Jaakkola, Kevin Yang, Kyle Swanson, and the first author Jonathan Stokes, they demonstrated how blending their backgrounds can yield potential answers to combat the growing antibiotic resistance crisis.

Collins saw the conference as an opportunity to inspire interest in antibiotic research, hoping to get the top young minds involved in battling resistance to antibiotics built up over decades of overuse and misuse, an urgent predicament in medicine that computer science students might not understand their role in solving. I think we should take advantage of the innovation ecosystem at MIT and the fact that there are many experts here at MIT who are willing to step outside their comfort zone and get engaged in a new problem, Collins says. Certainly in this case, the development and discovery of novel antibiotics, is critically needed around the globe.

AIDM showed the power of collaboration, inviting experts from major health-care companies and relevant organizations like Merck, Bayer, Darpa, Google, Pfizer, Novartis, Amgen, the U.S. Food and Drug Administration, and Janssen. Reaching capacity for conference attendees, it also showed people are ready to pull together to get on the same page. I think the time is right and I think the place is right, Collins says. I think MIT is well-positioned to be a national, if not an international leader in this space, given the excitement and engagement of our students and our position in Kendall Square.

A biotech hub for decades, Kendall Square has come a long way since big data came to Cambridge, Massachusetts, forever changing life science companies based here. AIDM kicked off with Institute Professor and Professor of Biology Phillip Sharp walking attendees through a brief history of AI in health care in the area. He was perhaps the person at the conference most excited for others to see the potential, as through his long career, hes watched firsthand the history of innovation that led to this conference.

The bigger picture, which this conference is a major part of, is this bringing together of the life science biologists and chemists with machine learning and artificial intelligence its the future of life science, Sharp says. Its clear. It will reshape how we talk about our science, how we think about solving problems, how we deal with the other parts of the process of taking insights to benefit society.

See the article here:
MIT conference reveals the power of using artificial intelligence to discover new drugs - MIT News

CHICAGO, April 28, 2020 /PRNewswire/ -- According to the new market research report "Artificial Intelligence in Agriculture Marketby Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026", published by MarketsandMarkets, the Artificial Intelligence in Agriculture Marketis estimated to be USD 1.0 billion in 2020 and is projected to reach USD 4.0 billion by 2026, at a CAGR of 25.5% between 2020 and 2026. The market growth is driven by the increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques.

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https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=159957009

By application, drone analytics segment projected to register highest CAGR during forecast period

The market for drone analytics is expected to grow at the highest rate due to its extensive use for diagnosing and mapping to evaluate crop health and to make real-time decisions. Favorable government mandates for the use of drones in agriculture are also expected to fuel the growth of the drone analytics market. Increasing awareness among farm owners regarding the advantages associated with AI technology is expected to further fuel the growth of the AI in agriculture market.

By technology, computer vision segment to register highest CAGR during forecast period

The increasing use of computer vision technology for agriculture applications, such as plant image recognition and continuous plant health monitoring and analysis, is one of the major factors contributing to the growth of the computer vision segment. The other factors include higher adoption of robots and drones in agriculture farms and increasing demand for improved crop yield due to the rising population. Computer vision allows farmers and agribusinesses alike to make better decisions in real-time.

Browsein-depth TOC on"Artificial Intelligence in Agriculture Market"81 Tables 40 Figures 152 Pages

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AI in agriculture market in APAC projected to register highest CAGR from 2020 to 2026

The AI in agriculture market in Asia Pacific is expected to witness the highest growth during the forecast period. The wide-scale adoption of AI technologies in agriculture farms is the key factor supporting the growth of the market in this region. AI is increasingly applied in the agriculture sector in developing countries, such as India and China. The increasing adoption of deep learning and computer vision algorithm for agriculture applications is also expected to fuel the growth of the AI in agriculture market in the Asia Pacific region.

International Business Machines Corp. (IBM) (US), Deere & Company (John Deere) (US), Microsoft Corporation (Microsoft) (US), Farmers Edge Inc. (Farmers Edge) (Canada), The Climate Corporation (Climate Corp.) (US), ec2ce (ec2ce) (Spain), Descartes Labs, Inc. (Descartes Labs) (US), AgEagle Aerial Systems (AgEagle) (US), and aWhere Inc. (aWhere) (US) are the prominent players in the AI in agriculture market.

Related Reports:

Artificial Intelligence Marketby Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025

Artificial Intelligence in Manufacturing Marketby Offering (Hardware, Software, and Services), Technology (Machine Learning, Computer Vision, Context-Aware Computing, and NLP), Application, Industry, and Geography - Global Forecast to 2025

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An illustration of COVID-19

By Julian Cardillo '14April 27, 2020

More than 50,000 academic articles have been written about COVID-19 since the virus appeared in November.

The volume of new information isnt necessarily a good thing.

Not all of the recent coronavirus literature has been peer reviewed, while the sheer number of articles makes it challenging for accurate and promising research to stand out or be further studied.

Computer science and linguistics professor James Pustejovsky is leading a Brandeis team in creating an artificial intelligence platform called Semantic Visualization of Scientific Data or SemViz that can sort through the growing mass of published work on coronavirus and help biologists who study the disease gain insights and notice patterns and trends across research that could lead to a treatment or cure.

Pustejovsky, an expert in theoretical and computational modeling and language, is partnering with colleagues at Tufts University, Harvard University, the University of Illinois, and Vassar College. He discussed his work with BrandeisNOW.

Can you provide a birds-eye view of the way youve applied your background as a computational linguist to current coronavirus research?

Im a researcher who focuses on language and extracting information from large amounts of text, like the COVID-19 dataset, which now includes more than 50,000 academic articles. Biologists on the front lines of coronavirus are trying to find connections between genes, proteins and drugs, and how they interact with the virus in the cells of the human body.

SemViz combs through the existing papers and manuscripts and enables scientists to make connections and generalizations that are not obvious from reading one paper at a time.

So how might a biologist studying coronavirus actually use SemViz?

This tool gives a rapid way for biologists studying coronavirus to see a global overview of inhibitors, regulators, and activators of genes and proteins involved in the disease.

For example, what are the drugs and proteins regulating the receptor for the COVID-19 virus? This could help discover therapies that decrease the expression of the receptor for the virus in patients lungs. This is important because millions of people currently take blood pressure medicines that can alter this receptor and possibly increase their risk of contracting the disease.

SemViz creates a visualization landscape that helps biologists make both global and specific connections between human genes, drugs, proteins and viruses. The overall program Im working on contains three components: two semantic visualization outputs based on the entire coronavirus research dataset, as well as a natural language-based question-answering application.

Whats the language application grid and how does it work?

It is essentially a computer-based reading machine that interprets tens of thousands of research articles on coronavirus and presents the results of this process to biologists in a form that is visually accessible and easily analyzed and interpreted.

It is more informative than a search engine, because it utilizes a host of language understanding tools and AI that can be applied to different domains (economics, news, science, literature) and text types (tweets, articles, books, email).

What are the implications of SemViz?

I think its hard to overstate the challenge brought about by information overload, particularly now with the coronavirus literature.

Biologists are interested in the mechanisms and functions of specific chemicals and proteins. SemViz can be the roadmap that scientists use to sort through large amounts of research to find these kinds of functions and relationships.

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How artificial intelligence is helping scientists find a coronavirus treatment - Brandeis University