ASSANGE EXTRADITION: Espionage is the Charge, But He’s Really Accused of Sedition – Consortium News

The U.S. is trying to extradite Julian Assange to stand trial for espionage, but even though sedition is no longer on the books, thats what the U.S. is really charging him with, says Joe Lauria.

By Joe LauriaSpecial to Consortium News

The United States has had two sedition laws in its history. Both were repealed within three years. Britain repealed its 17th Century sedition law in 2009. Though this crime is no longer on the books, the crime of sedition is really what both governments are accusing Julian Assange of.

The campaign of smears, the weakness of the case and the language of his indictment proves it.

The imprisoned WikiLeaks publisher has been indicted on 17 counts of espionage under the 1917 U.S. Espionage Act on a technicality: the unauthorized possession and dissemination of classified materialsomething that has been performed by countless journalists and publishers over the decades. It conflicts head on with the First Amendment.

But espionage isnt really what the government is after. Assange did not pass state secrets to an enemy of the United States, as in a classic espionage case, but rather to the public, which the government might well consider the enemy.

Deep Roots

Assange revealed crimes and corruption by the state. Punishing such legitimate criticism of government as sedition has deep roots in British and American history.

Tybun Tree, place of execution near where Marble Arch now stands in London.

Sedition was seen in the Elizabethan era as the notion of inciting by words or writings disaffection towards the state or constituted authority. Punishment included beheading and dismemberment.

In their efforts to suppress political discussion or criticism of the government or the governors of Tudor England, the Privy Council and royal judges needed a new formulation of a criminal offence This new crime they found in the offence of sedition, which was defined and punished by the Court of Star Chamber. If the facts alleged were true, that only made the offence worse, wrote historian Roger B. Manning. Sedition fell short of treason and did not need to provoke violence.

Though the Star Chamber was abolished in 1641, the British Sedition Act of 1661, a year after the Restoration, said, a seditious intention is an intention to bring into hatred or contempt, or to exite disaffection against the person of His Majesty, his heirs or successors, or the government and constitution of the United Kingdom.

Part of the 1798 U.S. Sedition Act.

Under President John Adams, the first U.S. Sedition Act in 1798 put it this way:

To write, print, utter or publish, or cause it to be done, or assist in it, any false, scandalous, and malicious writing against the government of the United States, or either House of Congress, or the President, with intent to defame, or bring either into contempt or disrepute, or to excite against either the hatred of the people of the United States, or to stir up sedition, or to excite unlawful combinations against the government, or to resist it, or to aid or encourage hostile designs of foreign nations.

While WikiLeaks publications have never been proven false, the U.S. government is certainly portraying its work as scandalous and malicious writing against the United States and has accused him of encouraging hostile designs against the country.

Congress did not renew the Act in 1801 and President Thomas Jefferson pardoned those serving sentences for sedition and refunded their fines.

Second US Sedition Act

When President Woodrow Wilson backed the Espionage Act in 1917 he lost by one Senate vote on an amendment that would have legalized government censorship.

So the following year Wilson pushed for another amendment to the Espionage Act. It was called the Sedition Act, added on May 16, 1918 by a vote of 48 to 26 in the Senate and 293 to 1 in the House.

1918 protest in front of the White House against the Sedition Act.

The media at the time supported the Sedition Act much as it works today against its own interests by abandoning the seditious Assange. Author James Mock, in his 1941 book Censorship 1917, said most U.S. newspapers showed no antipathy toward the act and far from opposing the measure, the leading papers seemed actually to lead the movement in behalf of its speedy enactment.

Among other things, the 1918 Sedition Act stated that:

whoever, when the United States is at war, shall willfully utter, print, write or publish any disloyal, profane, scurrilous, or abusive language about the form of government of the United States or the Constitution of the United States, or the military or naval forces of the United States, or the flag of the United States, or the uniform of the Army or Navy of the United States into contempt, scorn, contumely, or disrepute, or shall willfully utter, print, write, or publish any language intended to incite, provoke, or encourage resistance to the United States.

The U.S. has certainly seen revealing prima facia evidence of U.S. war crimes and corruption as being disloyal, profane, scurrilous, or abusive towards the U.S. government and military.

Debs & Assange

A month after the 1918 Sedition Act was passed, socialist leader Eugene Debs was sentenced to ten years in prison for publicly opposing the military draft. In a June 1918 speech he said: If war is right let it be declared by the people. You who have your lives to lose, you certainly above all others have the right to decide the momentous issue of war or peace.

While in jail Debs received one million votes for president in the 1920 election. Assanges defiance of the U.S. government went well beyond Debs anti-war speech by uncovering war crimes and corruption.

Debs at a 1918 rally, shortly before being arrested for sedition for opposing the draft. (Wikimedia Commons)

For being seditious, Debs and Assange are the most prominent political prisoners in U.S. history.

Despite an attempt by Attorney General A. Mitchell Palmer (of the anti-red Palmer Raids) to establish a peacetime Sedition Act, it was repealed in 1921but not before thousands of people were charged with sedition.

It was repealed because it was not seen as befitting a democratic society. Prosecuting Assange no longer arouses such widespread sentiment.

Except for a technicality in the Espionage Act, which needs to be challenged on constitutional grounds, the U.S. has no case against Assange. The weakness of the governments case points to it falling back on the abolished crime of sedition as the real, unstated charge.

The Accusations

The superseding indictment against Assange makes plain that official Washington is acting out of a fit of pique more than a sense of injustice. It is angry at Assange for revealing its dirty deeds.

He is seen not only as having acted disrespectfully to the U.S. government, but also stirring up popular opposition. In other words, he has committed an act of sedition. Because that crime is no longer on the books, it has to be described in a different way.

There is really only one technical infraction of the law that Assange is being accused of: unauthorized possession and dissemination. The rest of the indictment is about behavior that is not illegal, but what can be called seditious.

The Espionage Act indictment is so weak that it can only resort to an accusation that Assange endangered U.S. national security and aiding the enemy with no evidence to prove that that had ever happened.

Sedition Law Passed

Instead U.S. officials have been incensed with Assange for the embarrassment of uncovering their crimes and corruption. Since sedition is no longer on the books, they are only left with Section 793, paragraph (e) of the Espionage Act: the unauthorized possession and dissemination charge.

Innumerable journalists over the decades have possessed and disseminated classified information and continue to do so. Every citizen who has retweeted a WikiLeaks document has possessed and disseminated classified information. As the first journalist charged with this, a constitutional conflict is set up with the First Amendment, which will likely be challenged in court if Assange is extradited to the U.S. (A U.S. senator and a representative last month introduced a bill that would exempt journalists from paragraph (e)).

Left with no serious charge against him, the indictment is in line with the condemnation of Assange by U.S. officials, such as former U.S. Vice President Joe Biden, who called him a high-tech terrorist and a British judge who called him a narcissist.

In other words, Assange has insulted the powerful in the manner of an Elizabethan subject. Hes being accused of sedition, including stirring up dissent and unrest, such as in Tunisia, which sparked the Arab uprisings of 2010-2011.

Assange revealed what corporate media covers up: part of the long post-war U.S. history of overthrowing governments and using violence to spread its influence over the globe. He showed the U.S. motive is not spreading democracy but expanding its economic and geo-strategic interests. It is plainly seditious to do so contrary to a power-worshipping corporate media suppressing these historical facts.

Sedition is evidently a crime whose time has covertly come again.

Part Two in this series will be on The History of the Espionage Act and How it Ensnared Julian Assange.

Joe Lauria is editor-in-chief of Consortium News and a former correspondent for The Wall Street Journal, Boston Globe,Sunday Timesof London and numerous other newspapers. He began his professional career as a stringer for The New York Times. He can be reached atjoelauria@consortiumnews.com and followed on Twitter @unjoe .

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ASSANGE EXTRADITION: Espionage is the Charge, But He's Really Accused of Sedition - Consortium News

The Complete Beginners’ Guide to Artificial Intelligence

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 will be used to power cyberattacks, warn security experts – ZDNet

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

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

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

How Artificial Intelligence is Changing the Auto Industry – Legal Examiner

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

Artificial Intelligence in Agriculture Market Worth $4.0 Billion by 2026 – Exclusive Report by MarketsandMarkets – PRNewswire

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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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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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.

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Artificial Intelligence in Agriculture Market Worth $4.0 Billion by 2026 - Exclusive Report by MarketsandMarkets - PRNewswire

MIT conference reveals the power of using artificial intelligence to discover new drugs – MIT News

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.

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MIT conference reveals the power of using artificial intelligence to discover new drugs - MIT News

What Opportunities are Appearing Thanks to AI, Artificial Intelligence? – We Heart

The AI sector is booming. Thanks to several leaps that have been made, we are closer than ever before to developing an AI that acts and reacts as a real human would do. Opportunities in this sector are flourishing, and there is always a way for you to get involved.

Photo by Annie Spratt.

Employees: If you are searching for a job in the tech sector, one of the most rewarding you could find is working with AI. It is a mistake to assume that all AI development is focussed on developing android technologies. There are many other applications for AI and each one needs experts at the helm to help bring it to fruition.

Whether you are a graduate, or you are looking for a change in careers, there is always a job opening that you could look into. Even if you dont have a background in this tech, there are many other ways you could get involved, whether you are working on an AIs cognitive abilities or even just testing out the product. Whatever your background and skillset might be, there is always a way for you to get involved.

Investors: AI development is incredibly costly. While many of the smaller developers may have a great idea that could be world-changing if they bring it to fruition. However, they often lack the finances to be able to do so. This is where investors can come in.

Investors like Tej Kohli, James Wise, or Jonathan Goodwin may have little expertise in these areas from their own personal experience, but they know how to recognise a viable idea when presented with one. Whether you are looking to get into venture investment yourself or you are a tech company looking for financial backing, their activities should give you some idea about the paths you need to follow.

Photo, Bence Boros.

Consumers: The world of AI isnt just open to investors and tech gurus. There is now a vast range of AI-driven tech emerging onto the market. You, as a consumer, get to be an instrumental part of driving this new tech forward as it means that the developers gain some insight into what features are popular and which arent.

Just look at the boom in home assistants that has erupted in the past few years. We are now able to live in fully functioning smart homes with music playing and lights turning off with a simple voice command. By exploring what AI has to offer through the role of the consumer, this all feeds back to the developers and helps them create the next generation of products.

No matter how interested you are in this sector, there is always going to be something you can pursue that will help to develop AI overall. This is an incredibly exciting era to live in, and AI is just one of the pieces of tech that could transform the world as we know it. Take a look at some of the roles and opportunities and see where you could jump in today.

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What Opportunities are Appearing Thanks to AI, Artificial Intelligence? - We Heart

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

Photo/Getty Images

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

Mayo Clinic is using artificial intelligence in its COVID-19 research – KIMT 3

ROCHESTER, Minn. - Artificial intelligence has a vital role in helping researchers in their efforts to fight COVID-19 and is an important tool in the work being done at Mayo Clinic.

Dr. Andrew Badley is an infectious diseases specialist and leads Mayo Clinics COVID-19 Research Task Force. He explains thatthey created a real-time tracking platform to measure the rate of positive cases throughout all counties in Minnesota.

"When we did that, we noticed that there was an outlier which occurred in Martin County. The rate of a positive test in Martin County was approaching ten percent, whereas the rate of positive testing for most of the other counties was in the neighborhood of one or two percent. Based on that, we said were probably not doing enough testing in Martin County. We redeployed tests to that area. Weve deployed personal protective equipment to the healthcare workers in that area who were doing the tests. Quite rapidly we investigated, we identified a significant number of additional cases. After we identified those cases, we counseled on self-quarantining and therapy as indicated. And wed like to think that doing that activity has helped to prevent new transmission," said Dr. Badley.

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Mayo Clinic is using artificial intelligence in its COVID-19 research - KIMT 3