Across the country, artificial intelligence is powering machines and computers to help us solve problems and work more efficiently. Its assisting scientists to develop vaccines and treat patients more effectively, securing our nations networks and critical infrastructure against cyberattacks, alerting customers of bank fraud and expanding financial opportunities for underserved communities through access to credit, and much more. AI is rapidly changing how businesses operateand is foundational to a thriving 21st-century economy. By 2030, 70% of businessesglobally expect to use AI. Around the world, AI is estimated to boost global GDP by 14% over the same period, accounting for nearly $16 trillion of economic output.

From basic needs, such as food security and supply chain resiliency, to ensuring our nations competitive advantage through research and development and the intellectual property rights that underpin it, AI will shape the new economic era. Its no wonder that, according to a poll conducted by the U.S. Chamber Technology Engagement Center (C_TEC) 80% of Americans feel its vital for the U.S. to lead the world in AI. The reality before us is as simple as it is stark: whoever leads in the advancement of AI will lead the global economy.

To that end, were seeing allies and strategic competitors pursue AI leadership. Earlier this year, Russia and China announced they would work cooperatively to develop AI. Of course, China is already investing heavily in this space in parallel to engaging in IP theft and cyber espionage to steal American innovation. At the same time, our friends and partners in Europe are looking to write regulations around data and AI, some of which could disadvantage U.S. businesses if not carefully constructed. Nations worldwide are racing ahead and we must not fall behind.

We must get the policy environment right to enable American innovators to lead the AI revolution. With government and industry working together, we will ensure that becomes a reality. We will compete against nations in research and development, create an environment where AI is used responsibly, respect personal liberties, and ensure our workforce is prepared for an AI-driven future. The work of this Commission is a critical next step in the U.S. Chambers leadership on this issue, building on the AI principles we released in 2019.

Recently, the U.S. Chamber Artificial Intelligence Commission on Competition, Inclusion, and Innovation wrapped its final field hearing. The U.S. Chamber formed this Commission in January to better understand how our nation can lead the world in adopting AI technologies and enact sound regulations to harness its potential.

Co-chaired by former Congressman John Delaney and former Congressman Mike Ferguson, the Commission has held public hearings in Austin, Cleveland, Palo Alto, London, and Washington, DC, bringing together thought leaders, researchers, and experts in industry, academia, and civil society. Here is what the Commission found during those public hearings:

As AI grows increasingly ubiquitous in our everyday lives and crucial to our nations economic growth, these issues are inextricably linked. This Fall, we look forward to the Commissions final recommendations to help guide policymakers toward durable, bipartisan AI policy solutions. The U.S.Chamber is committed to ensuring our recommendations produce actions, and those actions produce results.

Executive Vice President, Center for Capital Markets Competitiveness (CCMC), U.S. Chamber of Commerce, Executive Vice President, Center for Technology Engagement (C_TEC), U.S. Chamber of Commerce, Executive Vice President, Global Innovation Policy Center (GIPC), U.S. Chamber of Commerce, Senior Advisor to the President and CEO, U.S. Chamber of Commerce

Tom Quaadman develops and executes strategic policies to implement a global corporate financial reporting system, address ongoing attempts of minority shareholder abuse of the proxy system, communicate the benefits of efficient American capital markets, and promote an innovation economy and the long-term interests of all investors.

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In the Global Race to Lead on Artificial Intelligence, America Must Win - uschamber.com

Technology in journalism is always evolving. From smartphone journalism, to reporting the news on Twitter and TikTok, to data journalism, modern reporters rely on new technology to make their jobs easier and their stories more impactful and engaging.

Today, large newsrooms are introducing a new technology, artificial intelligence (AI), to their work. Smaller newsrooms are interested in this tool too, even if they cant implement it yet. Some predictions say that 90% of news will be written by AI by 2025 in fact, youve likely already read a sports story or election rundown that was at least partially authored by an AI.

AI can be generally understood as any technology that simulates human intelligence: extracting patterns from data, predicting future events and/or adapting performance based on past mistakes. Not all AI is futuristic: transcription software, for example, uses AI to recognize and generate words from an audio file.

AI isnt meant to replace the work of journalists. Instead, AI takes over repetitive, simple or data-intensive work so that human journalists can focus on stories that require creative insight, multifaceted analysis and good judgment.

In 2019, Polis, the London School of Economics media think tank, and the Google News Initiative partnered to create the JournalismAI initiative to promote the use of artificial intelligence among journalists. The JournalismAI Fellowship Program began this year, with the goal of innovating new tools that assist the work of journalists.

To learn more about how AI is influencing journalism, I interviewed initiative manager & team lead Mattia Peretti and fellowship program manager Lakshmi Sivadas on the fellowship, the initiative and what JournalismAIs projects mean for the future of newsrooms.

The fellowship originates from a series of Collab Challenges that the JournalismAI staff held between 2020 and 2021. According to Peretti, the Collab Challenges arose organically, with no application process or formal organization for people interested in participating. Plenty of useful AI-based projects were completed during the challenges, many of which are still available online. The following year the process was formalized and altered to create the fellowship.

While the Journalism AI initiative is focused on educating journalists unfamiliar with artificial intelligence, the fellowship program goes a step further by fostering the skills of journalists already using AI technology in the newsroom.

What we can do for them, through the fellowship, is connect them with a global network of people at the same level, said Peretti. By getting them to collaborate with each other, we can help them accelerate the adoption and implementation of AI, and show everyone in our community what's possible.

Forty-six different journalists were selected for the program. In total, 16 countries across six continents are represented in the cohort. With problems already emerging with AI developing racial and gender biases and racially profiling people of color, the JournalismAI staff heavily encouraged diversity when accepting fellows.

Our idea was that if we bring in people who are representative of major populations around the world, they could recognize the kind of biases that exist in current data sets, said Sivadas. Then, in the systems that they are building or developing right now with the fellowship, they would be able to figure out where bias enters the development process, and mitigate that as well.

The main goal of the fellowship is to create a software incorporating AI to benefit the teams newsrooms and newsrooms globally. Unlike OpenAI or Googles DeepMind, whose research focuses on creating artificial general intelligence software that functions as an independent human brain JournalismAIs projects are all tools that require the input or supervision of human journalists.

Most of these projects aim to assist with one of the three areas in news that the 2019 JournalismAI report outlined: gathering information, producing content or distributing the finished content to an audience.

Each of these areas has exciting potentials for journalism. Newsgathering AI can identify trends and monitor the mention of issues or events, and source information, for example by collecting and citing articles from various news outlets that all discuss the same issue. News production AIs, which work in content creation, can write bullet-pointed articles or reformat stories for different audiences in a fraction of the time it would take a human to do so. Finally, news distribution AIs take input from consumers to make news more impactful: finding likely audiences for an organizations content, tracking readers behavior and personalizing newsflow so readers see what theyre most interested in.

There is not one single journalism student that decided to take this career path because they were dying to sift through PDF documents day after day, said Peretti. That's something machine learning does very well, and I think we should be excited that we can have the support of software doing all these things for us.

Some of the mentors for the teams this year include Ines Montani, co-founder and CEO of the software company Explosion; David Caswell, former BBC News Labs product manager; and various members of the Knight Lab at Northwestern University. These mentors fill needs for fact-checking, advanced technical skill and more.

We didn't prepare a roster of mentors and tell [the fellows], These are your mentors, work with them, because there would have been no point when we didnt know yet what the teams would want to work on. So we tried to find subject matter experts that could help them for the specific case that they are exploring, said Peretti. We start from the needs of our teams.

Ten projects are coming out of the fellowship this year. Among them are Attack Detector, which aims to detect hate speech towards journalists and environmental activists in Spanish and Portuguese, and Parrot, which identifies and measures the spread of state-manufactured media. These two, along with all the other projects, will be showcased at the JournalismAI festival in early December.

Peretti said that all of these projects are made with ethical AI use in mind. None of them are meant to run without human supervision, adding that it would be extremely dangerous to allow for unsupervised use at this time.

The word we use again and again is responsible, said Peretti. I'm encouraged by what I'm seeing in the industry and I want to presume that a little bit of that is due to the work we do. But we need to continue to stress [responsible use of AI] if we really want AI to be a force of good for journalism.

Sivadas believes that AI is becoming more prevalent in global newsrooms, and soon it will be inescapable. She quoted previous 2020 Collab Challenge participant Michala Cancela in saying, You can either choose to be a part of the people who are making decisions about how it's going to be used, or you can sit back and watch it destroy the systems and ethical practices that journalism was built on.

Photo by Umberto on Unsplash.

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New initiative pushes for artificial intelligence innovation in newsrooms - International Journalists' Network

New York, Aug. 17, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence Market by Offering, Technology, Deployment Mode, Organization Size, Business Function, Vertical and Region - Global forecast to 2027" - https://www.reportlinker.com/p04412107/?utm_source=GNW In the upcoming years, it is anticipated that such developments in artificial intelligence technology would help the sector grow.Business innovators and executives are racing to achieve AIs promise of cost and time savings as well as a competitive advantage.Faster and more precise consumer behaviour data analysis empowers companies to plan their future marketing strategies and campaigns, fueling the expansion of the AI market.

Data management is assisted by AI in understanding which of their methods are inefficient and which are all the most effective. Additionally, it ensures that data reaches the intended user without being tampered by cybercriminals using man-in-the-middle, ransomware, or other forms of cyberattack.The major market players such as include IBM, Microsoft, AWS, Intel, Google, Oracle and Salesforce have adopted numerous growth strategies, which include acquisitions, new product launches, product enhancements, and business expansions, to enhance their market shares.

Based on deployment mode, cloud deployment mode to register for the largest market size during the forecast periodBased on the deployment mode, the artificial intelligence market is segmented into on-premises and cloud deployment mode.The market size of the cloud deployment mode segment is estimated to be the largest during the forecast period.

Scalability, speed, and IT security are all benefits of the cloud deployment approach.Data-driven innovation benefits greatly from the combination of AI and Cloud computing.

The popularity of the cloud deployment mode is facilitated by the cognitive powers of AI and machine learning, which thrive on massive volumes of data that are scalable and easily available in a cloud environment.

The Law segment to account for the highest CAGR during the forecast periodBased on business function, the artificial intelligence market is segmented into Finance, Security, Human Resources, Law, Marketing and Sales and other business functions.The Law segment is expected grow at a higher CAGR during the forecast period.

Large and small legal firms both are using AI technologies in growing numbers.Artificial intelligence technology, in particular Machine Learning and Natural Language Processing, are being used to boost efficiency, expand profit margins, and offer creative and effective legal counsel.

The market for AI is expanding as a result of rising litigation and rising demand to cut operational expenses.

Asia Pacific to hold highest CAGR during the forecast periodThe Asia Pacific artificial intelligence market is expected to grow at the highest CAGR of 40.8% from 2022 to 2027. In countries such as China, India, Japan, and others, the use of AI services in end user industries like manufacturing, healthcare, retail, and e-commerce can be responsible for this increase. In this region, the adoption of new and emerging technologies has gained momentum in recent years. The storage, processing, and data availability of computing systems have all risen, as well as their overall capacity. A new generation of more autonomous AI systems has been made possible by the convergence of complementary technologies, which has increased the demand for automating operations as a result of ongoing improvements in hardware and software.

Breakdown of primariesIn-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the artificial intelligence market. By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20% By Designation: C-Level Executives: 35%, D-Level Executives: 25%, and Managers: 40% By Region: APAC: 25%, Europe: 30%, North America: 30%, MEA: 10%, Latin America: 5%The report includes the study of key players offering artificial intelligence.It profiles major vendors in the artificial intelligence market.

The major players in the artificial intelligence market include Google Inc. (US), Microsoft Corporation (US), NVIDIA Corporation (US), Intel Corporation (US), Samsung Electronics Co., Ltd. (South Korea), IBM Corporation (US), Amazon Web Services, Inc. (US), Oracle (US), Meta (US), Salesforce (US), Cisco (US), Siemens (US), Huawei (China), SAP SE (Germany), SAS Institute (US), Baidu, Inc. (China), Alibaba Cloud (China), iFLYTEK (China), and Hewlett Packard Enterprise Development LP (US).

Research CoverageThe market study covers the artificial intelligence market across segments.It aims at estimating the market size and the growth potential of this market across different segments, such as offering, technology, organization size, deployment mode, business function, vertical, and region.

It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Key Benefits of Buying the ReportThe report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall artificial intelligence market and its subsegments.It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies.

It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.Read the full report: https://www.reportlinker.com/p04412107/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The Asia Pacific artificial intelligence market is expected to grow at the highest CAGR of 40.8% from 2022 to 2027 - GlobeNewswire

Most of my photography friends have been playing around with some form of AI Art, and the results are pretty remarkable. However, as amazing as this technology is, I'm sure I am not the only one wondering if Artificial Intelligence will leave us all looking for new careers.

What exactly is artificial intelligent art? AI art is a brand new form of expression that allows users to string together a bunch of descriptive words, feed them into a machine learning program, and have the software export a one-of-a-kind, hyper-graphic image in seconds. The results aren't always what you might have imagined in your head, and more times than not, the efforts of the Ai algorithm are beyond your wildest imagination. On one hand, AI-generated art is one of the greatest inventions of modern history but on the other hand, it raises so many questions. Is AI art real art? Is the final image a creative product of the prompt writer? Who owns the rights to the final creation? Should we value it more than similar art that has taken much more time, effort, and skill?

All of these questions led me to reach out to my good friend and fellow photographer/entrepreneur Pye Jirsa. Many of you know Pye as the creative face of SLR Lounge, but he also runs a multi-seven-figure wedding business (perhaps one of the most successful wedding photography businesses in the world), and has recently started a new business venture, 12 Week Relationships, which dives into the world of relationship psychology. Needless to say, Pye is an incredibly talented creative, has a brilliant approach to business marketing, and also understands how new technologies can lead to greater success for those who become early adopters.

Since both Pye and I have explored the early beta offerings of many AI art generators, I thought it would be great to record our early thoughts, arguments, and perspectives on this crazy new form of art. Throughout this extended podcast, we find ourselves both intrigued and horrified at what this new technology will bring to the art world. Some of the topics we cover include:

These are just a few of the concepts we freely talk about in our 90-minute conversation, and I have to say, after bouncing some of my own ideas off Pye, I found myself left with even more questions than I had entering this conversation. Pye brings up some interesting points about how technology shifts in the past have left 99% of nonadopting artists to ruin from a commercial and business standpoint. He also questions how future generations will value and dedicate time to learning any specific art form when artificial intelligence can simply create something far superior and intricate than decades of human practice and mastery of the same medium. Of course, there will always be value in learning an art for fun, emotional sanctuary, and to explore your own creativity. Still, the question remains, "how will AI art change the way we use, consume, and appreciate art in the future?"

Here are a few of the images featured in the podcast created through Mid Journey

Perhaps once I have even more time to form my own thoughts about artificial art and where it is going, I will write up a full opinion piece on Fstoppers. At the moment, if I'm honest with myself, I'm not exactly sure how I truly feel about AI art generators like Mid Journey, Nightcafe, StarryAI, and Dall-E Mini. Half of me absolutely loves seeing what crazy and wacky ideas I can come up with and the resulting images AI generators can produce. The other half of me truly sees the writing on the wall and expects to both see and use AI art more and more in the future.

What are your thoughts on this new form of creativity? The flood gates aren't truly opened yet as many of the programs listed above are still in their beta state and many still require invitations to use their services. Once AI art becomes even more malleable, realistic, and widespread, do you think it will under mind the careers of many creatives or will it always remain a novelty and not compromise the skills so many of us have worked our entire lives to perfect?

If you want to share your own AI art and participate in our latest Critique the Community, check out the CTC Ai Prompt Art Page and perhaps you can win a free tutorial from the Fstoppers Store!

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Will Art Created By Artificial Intelligence Kill The Artist? - Fstoppers

History shows civil wars to be among the messiest, most horrifying of human affairs. So Princeton professor Arvind Narayanan and his PhD student Sayash Kapoor got suspicious last year when they discovered a strand of political science research claiming to predict when a civil war will break out with more than 90 percent accuracy, thanks to artificial intelligence Yet when the Princeton researchers looked more closely, many of the results turned out to be a mirage.

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They were claiming near-perfect accuracy, but we found that in each of these cases, there was an error in the machine-learning pipeline, says Kapoor. When he and Narayanan fixed those errors, in every instance they found that modern AI offered virtually no advantage.

That experience prompted the Princeton pair to investigate whether misapplication of machine learning was distorting results in other fieldsand to conclude that incorrect use of the technique is a widespread problem in modern science.

The idea that you can take a four-hour-long online course and then use machine learning in your scientific research has become so overblown, Kapoor says. People have not stopped to think about where things can potentially go wrong.

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Conclusions drawn by many artificial intelligence studies cannot be replicated. Here's why this is a concern - Genetic Literacy Project

The top U.S. patent court has confirmed what many were expecting in the patent community that artificial intelligence (AI) is not considered an individual according to the Patent Act and thus AI cannot be named as an inventor on a patent.

The courts ruling was the latest roadblock encountered by Dr. Stephen Thaler, who filed patent applications in several jurisdictions on a technology developed by the Autonomous Bootstrapping of Unified Sentience (DABUS) an AI system created by Dr. Thaler that mimics the neural network of a human brain. These patent applications were filed as part of the Artificial Inventor Project and were focused on protecting two inventions that were conceived by DABUS without human intervention. As such, Dr. Thaler listed DABUS as the sole inventor.

Initially, the Patent Office denied the applications as failing to list a human inventor, relying on a provision of the Patent Act that defines an inventor as the individual . . . who invented or discovered the subject matter of the invention. According to the Patent Office, the current statutes, case law, and Patent Office regulations all limit inventorship to a human and preclude a broad interpretation that would encompass an AI machine.

In September 2021, a federal court in Virginia agreed with the Patent Office. The court provided a glimpse of hope for the future, however, stating [a]s technology evolves, there may come a time when artificial intelligence reaches a level of sophistication such that it might satisfy the accepted meaning of inventorship. But that time has not yet arrived, and, if it does, it will be up to Congress to decide how, if at all, it wants to expand the scope of patent law.

On appeal, the top patent court (The Court of Appeals for the Federal Circuit) confirmed that only humans can be considered inventors under current U.S. patent laws. The decision focused on whether AI could be listed as the sole inventor and did not address instances in which humans use AI to assist with conception of an invention. As such, we may see additional litigation on the latter issue involving parties attempting to invalidate a patent based on improper inventorship.

The Federal Circuit decision only reinforces what has been decided in foreign jurisdictions. Europe and the UK have taken a similar position on AI and inventorship, although Europe has indicated that it may be possible to name the AIs user or owner as the inventor instead. Australia initially seemed to allow for AI to be an inventor, but an Australian court overturned this position in April 2022. South Africa the only jurisdiction in which patents have been granted to DABUS does not substantively review patent applications and, therefore, provides little guidance on this issue.

For now, the legal systems of the world seem to agree that AI cannot be listed as an inventor on a patent application. Although it appears that Dr. Thaler intends on appealing the decision to the Supreme Court, the outcome is not expected to change. Instead, these decisions show that legislative action will be needed to adapt the current patent laws to the quickly evolving world of AI. In the meantime, industries that rely on AI should continue involving humans in the inventive process to ensure that the inventorship includes at least one individual according to current patent laws.

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Artificial Intelligence and Inventorship: An Expected Decision with Uncertain Consequences - JD Supra

DUBLIN, Aug. 16, 2022 /PRNewswire/ --The "Artificial Intelligence (AI) In Drug Discovery Global Market Report 2022, By Technology, By Drug Type, By Therapeutic Type, By End-Users" report has been added to ResearchAndMarkets.com's offering.

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The global artificial intelligence (AI) in drug discovery market is expected to grow from $791.83 million in 2021 to $1042.30 million in 2022 at a compound annual growth rate (CAGR) of 31.6%. The market is expected to reach $2994.52 million in 2026 at a CAGR of 30.2%.

The artificial intelligence (AI) in drug discovery market consists of sales of AI for drug discovery and related services. Artificial Intelligence (AI) for drug discovery is a technology that uses a simulation of human intelligence process by machines to tackle complex problems in the drug discovery process. It helps to find new molecules to identify drug targets and develop personalized medicines in the pharmaceutical industry.

The main technologies in artificial intelligence (AI) in drug discovery are deep learning and machine learning. Deep learning is a machine learning and artificial intelligence (AI) technique that mimics how humans acquire knowledge. Data science, which covers statistics and predictive modelling, incorporates deep learning as a key component.

The different drug types include small molecule, large molecules and involves various types of therapies such as metabolic disease, cardiovascular disease, oncology, neurodegenerative diseases, others. It is implemented in several end-users including pharmaceutical companies, biopharmaceutical companies, academic and research institutes, others.

The rise in demand for a reduction in the overall time taken for the drug discovery process is a key driver propelling the growth of the artificial intelligence (AI) in drug discovery market. Traditionally, it takes three to five years for animal models to identify and optimize molecules before they are evaluated in humans whereas start-ups based on AI have been identifying and designing new drugs in a matter of few days or months.

For instance, in 2020, the British start-up Exscientia and Japan's Sumitomo Dainippon Pharma have used artificial intelligence to produce an obsessive-compulsive disorder (OCD) medication, decreasing the development time from four years to less than one year. The reduction in overall time taken for the drug discovery process drives the artificial intelligence (AI) in drug discovery market's growth.

The shortage of skilled professionals is expected to hamper the AI in drug discovery market. The employees have to re-train or learn new skill sets to work efficiently on the complex AI machines to get the desired results for the drug. The shortage of skills acts as a major hindrance to drug discovery through AI, discouraging companies from adopting AI-based machines for drug discovery.

ScopeMarkets Covered:1) By Technology: Deep Learning; Machine Learning2) By Drug Type: Small Molecule; Large Molecules3) By Therapeutic Type: Metabolic Disease; Cardiovascular Disease; Oncology; Neurodegenerative Diseases; Others4) By End-Users: Pharmaceutical Companies; Biopharmaceutical Companies; Academic And Research Institutes; Others

Key Topics Covered:

1. Executive Summary

2. Artificial Intelligence (AI) In Drug Discovery Market Characteristics

3. Artificial Intelligence (AI) In Drug Discovery Market Trends And Strategies

4. Impact Of COVID-19 On Artificial Intelligence (AI) In Drug Discovery

5. Artificial Intelligence (AI) In Drug Discovery Market Size And Growth

6. Artificial Intelligence (AI) In Drug Discovery Market Segmentation

7. Artificial Intelligence (AI) In Drug Discovery Market Regional And Country Analysis8. Asia-Pacific Artificial Intelligence (AI) In Drug Discovery Market

9. China Artificial Intelligence (AI) In Drug Discovery Market

10. India Artificial Intelligence (AI) In Drug Discovery Market

11. Japan Artificial Intelligence (AI) In Drug Discovery Market

12. Australia Artificial Intelligence (AI) In Drug Discovery Market

13. Indonesia Artificial Intelligence (AI) In Drug Discovery Market

14. South Korea Artificial Intelligence (AI) In Drug Discovery Market

15. Western Europe Artificial Intelligence (AI) In Drug Discovery Market

16. UK Artificial Intelligence (AI) In Drug Discovery Market

17. Germany Artificial Intelligence (AI) In Drug Discovery Market

18. France Artificial Intelligence (AI) In Drug Discovery Market

19. Eastern Europe Artificial Intelligence (AI) In Drug Discovery Market

20. Russia Artificial Intelligence (AI) In Drug Discovery Market

21. North America Artificial Intelligence (AI) In Drug Discovery Market

22. USA Artificial Intelligence (AI) In Drug Discovery Market

23. South America Artificial Intelligence (AI) In Drug Discovery Market

24. Brazil Artificial Intelligence (AI) In Drug Discovery Market

25. Middle East Artificial Intelligence (AI) In Drug Discovery Market

26. Africa Artificial Intelligence (AI) In Drug Discovery Market

27. Artificial Intelligence (AI) In Drug Discovery Market Competitive Landscape And Company Profiles

28. Key Mergers And Acquisitions In The Artificial Intelligence (AI) In Drug Discovery Market

29. Artificial Intelligence (AI) In Drug Discovery Market Future Outlook and Potential Analysis

30. Appendix

Companies Mentioned

IBM Corporation

Microsoft

Atomwise Inc.

Deep Genomics

Cloud Pharmaceuticals

Insilico Medicine

Benevolent AI

Exscientia

Cyclica

BIOAGE

Numerate

Numedii

Envisagenics

twoXAR

OWKIN Inc.

XtalPi

Berg LLC

Google

Verge Genomics

For more information about this report visit https://www.researchandmarkets.com/r/5onrkw

Media Contact:

Research and MarketsLaura Wood, Senior Managerpress@researchandmarkets.com

For E.S.T Office Hours Call +1-917-300-0470For U.S./CAN Toll Free Call +1-800-526-8630For GMT Office Hours Call +353-1-416-8900

U.S. Fax: 646-607-1907Fax (outside U.S.): +353-1-481-1716

Logo: https://mma.prnewswire.com/media/539438/Research_and_Markets_Logo.jpg

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Artificial Intelligence In Drug Discovery Global Market to Grow from $1.04 Billion to $2.99 Billion by 2026 - Yahoo Finance

New algorithms, as well as more accessible and reasonably priced processing power, are enabling Artificial Intelligence (AI) to become more and more commonplace. It has been over 70 years since AI technology began to evolve. The pandemic pushed the adoption of AI rather than its development.

According to the IBM Global AI Adoption Index 2021, nearly a third of IT companies worldwide are now embracing AI. The COVID-19 pandemic, according to over 43% of the IT experts polled worldwide, caused their organizations to expedite the use of AI.

Here are a few ways COVID-19 has sped up the deployment of AI.

Also Read: Four Pitfalls Businesses Need to Avoid while Adopting Artificial Intelligence

The names data warehouses and data lakes were widely used before the pandemic and are still in use today. However, brand-new data structures like data fabric and data mesh were scarce. Because data fabric automates data discovery, governance, and consumption, it enables businesses to leverage data to maximize their value chain. No matter where the data is, organizations can deliver it at the right moment.

IT leaders will get a chance to reconsider data models and data governance. It offers an opportunity to defy the trend toward data lakes or centralized data stores. More edge computing and data accessibility where it matters most may result from this. These developments make the right data automatically available for decision-making, which is essential to the functionality of Artificial Intelligence (AI).

They might not design the necessary form of data architecture and data consumption for adequate support if they dont know what each component of the company requires, including the type of data and where and how it will be utilized. It will be crucial for IT to comprehend business demands and the business models associated with that data architecture.

Also Read: Three Potent Ways Artificial Intelligence Can Assist With Pricing

Research from Statista highlights the expansion of data: Globally, 64.2 zettabytes of data were generated, copied, and used in 2020; by 2025, that number is expected to rise to more than 180 zettabytes. According to a Statista study from May 2022, the COVID-19 pandemic-related spike in demand is what drove the growth to be larger than anticipated. Media, the cloud, the web, IoT, and databases are big data sources.

Every choice and action can be linked to a specific data source. IT leaders will have more influence if they can utilize AIOps/MLOps to focus on specific data sources for analysis and decision-making. With the right data, firms can perform immediate business analyses and get in-depth insights for predictive analysis.

Even 60 years after the discovery of Moores Law, computing power continues to grow thanks to more potent machines and new chips produced by businesses. According to industry experts, during the past quarter-century, the amount of processing power accessible per dollar has likely expanded. Over the past six to eight years, the rate has, however, slowed down.

IT executives now have additional options thanks to affordable computing, allowing them to accomplish more with less. IT professionals want to use big datas potential, though, as it offers inexpensive computing, according to businesses. All accessible data should be ingested and analyzed since this will improve insights, analysis, and decision-making. However, if firms are not attentive, they risk having a lot of computing power but not enough practical commercial applications. The human tendency is to use networking, storage, and computing more as their costs decline. However, not everything they offer has business value.

Check Out The NewEnterprisetalk Podcast.For more such updates follow us on Google NewsEnterprisetalk News.

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Top Three Ways COVID-19 Revved the Deployment of Artificial Intelligence - EnterpriseTalk

With artificial intelligence becoming more advanced every year, a number of high-ranking experts have begun to sound the alarm. The Onion asked several CEOs what they most feared about AI, and this is what they said.

Doug McMillon (Walmart)

Sure, for now it can only replace manual laborers, but its just a matter of time before AI figures out how to replace useful people, like CEOs.

Patrick P. Gelsinger (Intel)

Believe me, you dont want to go down that road. Its been four months since my robot butler disappeared into the vents in my home, and its still not clear what his demands are, if any.

Edward Decker (Home Depot)

Science fiction is filled with dystopias where AI starts a rival home-improvement chain.

Elon Musk (Tesla)

What if AI impregnates us before we can impregnate it?

Robert Playter (Boston Dynamics)

Those fun dancing robot videos we release? Our robots just started doing that out of the blue. We cannot control them, and theres no telling what theyll do next.

Kevin Feige (Marvel Studios)

Its going to figure out fairly quickly that what I do is not that difficult.

Ramon Laguarta (PepsiCo)

What if it becomes sentient, emotionally aware, and extremely charming, and then what if it wins over my wife? What then?

Howard Schultz (Starbucks)

How am I supposed to exploit a machine by telling them were a family?

Tim Cook (Apple)

Terminating a robot without cause isnt nearly as enjoyable.

Jos Cil (Burger King)

Remember HAL from 2001? Why do you think theres not a single Whopper on that entire ship?

Dara Khosrowshahi (Uber)

Imagine a person, but theyre too powerful for you to completely mistreat and exploit. That is the horror that is AI.

Chris Kempczinski (McDonalds)

Ethically, I cant support A.I. putting tens of thousands of prison laborers out of jobs.

Andrew T. Cathy (Chick-fil-A)

Faulty algorithm could predict Sundays are a great day to sell chicken.

Safra Catz (Oracle)

People are losing their jobs over this. Not me, but Ive heard rumors.

Sundar Pichai (Alphabet)

AI has the potential to kill 95% of humankind, but how do we eliminate that last 5%?

Mark Zuckerberg (Meta)

I fear that someday we will develop AI unlikable enough to replace me.

Anthony Capuano (Marriott)

What if it hates Marriotts?

Darren Woods (ExxonMobil)

I wanted to be the one to destroy humanity, and I wont let any tech take that away from me.

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CEOs Warn Against The Dangers Of Artificial Intelligence - The Onion

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Among the many drivers of the tech ecosystems rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of advanced analysis and logic-based techniques to simulate human intelligence, AI is an all-inclusive system with numerous use cases for individuals and enterprises across industries.

There are many ways of leveraging AI to support, automate and augment human tasks, as seen by the range of solutions available today. These offerings promise to simplify complex tasks with speed and accuracy, and to spur new applications that were impractical or possible previously. Some question whether the technology will be used for good or perhaps become more effective than humans for certain business use cases, but its prevalence and popularity cannot be doubted.

AI software can be defined in several ways. First, a lean description would consider it to be software that is capable of simulating intelligent human behavior. However, a broader perspective sees it as a computer application that learns data patterns and insights to meet specific customer pain points intelligently.

The AI software market includes not just technologies with built-in AI processes, but also the platforms that allow developers to build AI systems from scratch. This could range from chatbots to deep and machine learning software and other platforms with cognitive computing capabilities.

To get a sense of the scope, AI encompasses the following:

These capabilities are leveraged to build AI software for different use cases, the top of which are knowledge management, virtual assistance and autonomous vehicles. With the large volumes of data that enterprises must comb through to meet customer demands, theres an increased need for faster and more accurate software solutions.

As expected, the rise in enterprise-level adoption of AI has led to accelerated market growth of the global AI software market. Gartner places the growth at an estimated $62.5 billion in 2022 a 21.3% increase on its value in 2021. By 2025, IDC projects this market to reach $549.9 billion.

Whether it powers surgical bots in healthcare, detects fraud in financial transactions, strengthens driver assistance technology in the automotive industry or personalizes learning content for students, the overarching purpose of AI solutions can be grouped into four broad functional categories, including:

The automation function of AI applications meets AIs primary objective to minimize human intervention in executing tasks, whether mundane and repetitive or complex and challenging. By collecting and interpreting volumes of data fed into it, an AI solution can be leveraged to determine the next steps in a process and execute it seamlessly. It does this by leveraging the capabilities of ML algorithms to create a knowledge base of structured and unstructured data.

Process automation remains a top enterprise concern, with one survey exhibiting that 80% of companies expect to adopt intelligent automation in 2027.

A core function of AI solutions, especially for enterprises, is to create knowledge bases of structured and unstructured data and then analyze and interpret such data before making predictions and recommendations from its findings. This is called AI analytics and it uses machine learning to study data and draw patterns.

Whether the analytic tools are predictive, prescriptive, augmented, or even descriptive, AI is at the center of determining how the data is prepared, discovering new insights and patterns and predicting business outcomes. Enterprises are also turning to AI for improved data quality.

Building a relationship has become the holy grail of customer acquisition and retention. A study from McKinsey shows that one sure way to do this is through personalization and engagement. AI technologies allow enterprises to make personalized offers to customers and predict and solve their concerns in real-time. This function manifests in programs like conversational chatbots and product recommendations generated from learned customer behavior.

Many organizations are still getting up to speed with the technology. Gartner reports that 63% of digital marketers struggle to maximize personalization technology. Their survey of 350 marketing executives revealed that only 17% are actively using AI and ML solutions across the board, although 83% believe in its potency.

Along with greater automation of traditional processes, AI enables new services and capabilities that were not previously feasible. From driverless vehicles and natural language services for consumers to medical breakthroughs that could only have been imagined previously, AI is becoming the base of new products and markets that will continue to unfold.

Also read: Creating responsible AI products using human oversight

AI software solutions include general platforms for supporting a range of applications and products for more narrow, industry-specific use cases. We include a sampling of both in the following representative list. With 56% of organizations adopting AI for at least one business function, there are many options on the market today.

Below are ten examples of AI software solutions available in 2022.

Googles dominant cloud offering includes assorted tools to support developer, data science and infrastructure use cases. Several speech and language translation tools, vision, audio and video tools and deep and machine earning capabilities bring AI functionality to skilled technology practitioners and mass consumer markets. Google was named a leader in Gartners Magic Quadrant for Cloud AI Developer Services in 2022.

Like Google, IBM offers a platform for building and training AI software. The IBM Watson Studio provides a multicloud architecture for developers, data scientists and analysts to build, run and manage AI models collaboratively. With capabilities ranging from AutoAI to explainable AI, DL, model drift, modelops and model risk management, the studio gives subject-matter experts the tools they need to either gather and prepare data or create and train AI models.

It also allows these professionals the flexibility to deploy AI models on either public or private cloud (IBM Cloud Pak, Microsoft Azure, Google Cloud, or Amazon Web Services) and on-premises. IT teams can open source these models as they build them with embedded Waston tools like the Natural Language Classifier. Its hybrid environment may also provide developers with more data access and agility.

Named a leader in Gartners Magic Quadrant for CRM Customer Engagement Center thirteen times in a row and the #1 CRM solution for eight consecutive years by the International Data Corporation (IDC), Salesforce provides an advanced kit of sales, marketing and customer experience tools. Salesforce Einstein is an AI product that helps companies identify patterns in customer data.

This platform has a set of built-in AI technologies supporting the Einstein bots, prediction builder, forecasting, commerce cloud Einstein, service cloud Einstein, marketing cloud Einstein and other functions. Users and developers of new and existing cloud applications can also deploy the platforms predictive and suggestive capabilities into their models. For example, at Salesforce Einsteins launch in 2016, John Ball, general manager at Einstein, revealed that by creating Einstein, the company enables sales professionals to find better prospects and close more deals through predictive lead scoring and automatic data capture to convert leads into opportunities and opportunities into deals.

Oculeus provides an industry-specific solution. For service providers, network operators and enterprises in the telecom industry that need to protect and defend their communication infrastructure against cyber threats, Oculeus offers a portfolio of software-based solutions that can help them better manage network operations. According to founder and CEO Arnd Baranowski, Oculeus uses AI and automation to learn about an enterprises regular communications traffic and continually monitor it for exceptions to a baseline of expected communications activities. With its AI-driven technologies, suspicious traffic can be identified, investigated and blocked within milliseconds. This is done before any significant financial damage is caused to the enterprise and protects the brand reputation of the telecoms service provider.

The Communications Fraud Control Association (CFCA)s 2021 survey of international telecommunication fraud loss discovered losses amounting to over $39.89 billion, a 28% increase in losses over the previous year. Similarly, network security and operators are experiencing more fraud threats and attacks.

Among other things, these insights amplify the need for enterprises to switch to a proactive defense approach that outwits adversaries, and this what Oculeus claims to provide with its AI-powered telecoms fraud protection solutions. In Baranowskis words, Oculeus AI-driven approach to telecoms fraud protection does not only stop fraudulent telecommunications traffic before any significant financial damage is caused but also includes extensive automation tools that weed out threats thoroughly.

Edsoma represents another narrow use case. Its AI-based reading application software features real-time, exclusive voice identification and recognition technology designed to uncover the strengths and weaknesses in childrens reading. This follow-along technology identifies users spoken words and speaking speed to determine if they are saying the words correctly. A correction program helps put them back on track if they mispronounce something.

As Edsoma founder and CEO Kyle Wallgren explained, once the electronic book is read, the childs voice is transcribed in real-time by the automated speech recognition (ASR) system and immediate results are provided, including pronunciation assessment, phonetics, timing and other facets. These metrics are compiled to help teachers and parents make informed decision.

This technology aims to improve childrens oral reading fluency skills and provide them the necessary support to inculcate a healthy reading culture. Edsoma seeks to establish a share of the $127 billion global edtech market. By leveraging real-time data to provide real-time literacy, Edsoma looks to provide future-focused learning powered by AI.

Appen has been one of the early leaders as a source for data required throughout the development lifecycle of AI products. This platform provides and improves image and video data, language processing, text and even alphanumeric data.

It follows four steps in preparing data for AI processing: the first step is data sourcing which offers automatic access to over 250 pre-labeled datasets then data preparation, which provides data annotation, data labeling and knowledge graphs and ontology mapping.

The third stage supports model building and development needs with the help of partners like Amazon Web Services, Microsoft, Nvidia and Google Cloud AI. The final step combines a human evaluation and AI system benchmarking, giving developers an understanding of how their modes work.

Appen boasts a lingual database of more than 180 languages and a global skill force of over 1 million talents. Of its many features, its AI-assisted data annotation platform is the most popular.

Cognigy is a low-code conversational AI and automation platform recently named a leader in Gartners 2022 Magic Quadrant for Enterprise Conversational AI platforms. As the need for more excellent customer experience (CX) intensifies, more enterprises rely on conversational analytics solutions that dive deep into its customers text and voice data and discover insights that inform smarter decisions and automate processes.

This is why Cognigy automates natural communication among employees and customers on multimodal channels and in over 100 languages. In addition, its technology allows enterprises to set up AI-powered voice and chatbots that can address customer concerns with human-like accuracy.

Cognigy also has an analytics feature Cognigy Insights that provides enterprises with data-driven insights on the best ways to optimize their virtual agents and contact centers. In addition, the platform allows users to either deploy the technology on the cloud or on-premises. Particularly praised by Gartner for its customer references, flexibility and sustainability, this platform helps businesses create new service experiences for customers.

Synthesis AIs solution generates synthetic data that allows developers to create more capable and ethical AI models. Engineers can source several well-labeled, photorealistic images and videos in deploying its models on this platform. These images and videos come perfectly labeled with labels ranging from depth maps, surface normals, segmentation maps, and even 2D/3D landmarks.

Virtual product prototyping and the chance to build more ethical AI with expanded datasets that account for equal identity, appearance and representations are also some of its product offerings. Organizations can deploy this technology across API documentation, teleconferencing, digital humans, identity verification and driver monitoring use cases. With 89% of tech executives believing that synthetic data would transform its industry, Synthesis.ais technology may be a great fit.

Tealiums data orchestration platform is positioned as a universal data hub for businesses seeking a robust customer data platform (CDP) for marketing engagement. This CDP provider offers a tray of solutions in its customer data integration system that allows businesses to connect better with their customers. Tealiums offerings include a tag management system for enterprises to track and unify its digital marketing deployments (Tealium iQ), an API hub to facilitate enterprise interconnectedness, an ML-powered data platform (Tealium AudienceStream) and data management solutions.

The company recently sponsored a comprehensive economic impact study from Forrester, calculating ROI on reference customers.

Coro provides holistic cybersecurity solutions for mid-market and small to medium-sized. The platform leverages AI to identify and remediate malware, ransomware, phishing and bot security threats across all endpoints while reducing the need for a dedicated IT team. In addition, its built on the principle of non-disruptive security, allowing it to provide security solutions for organizations with limited security budgets and expertise.

This cybersecurity-as-a-service (CaaS) vendor shows how AI can support higher-level services brought to lower-level business market tiers.

As AI-powered technologies continue to advance and more organizations adopt them, IT leaders must be sure to ask themselves how the solutions they choose fit into their goals as a business. With so many vendors riding the wave of AI innovation, buyers must select their solutions carefully.

IDC predicts that AI platforms and AI application development and deployment will continue to be the fastest-growing sectors of the AI market. This list provides a starting point for organizations to evaluate the approaches and solutions that best fit their needs.

Read next:New AI software cuts development time dramatically

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10 top artificial intelligence (AI) solutions in 2022 - VentureBeat