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CLEVELANDCollaboration between pharmaceutical companies and the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University has led to the development of artificial intelligence (AI) tools to benefit patients with non-small cell lung cancer (NSCLC) based on an analysis of routine tissue biopsy images, according to new research.

This year, more than 236,000 adults in the United States will be diagnosed with lung cancerabout 82% of them with non-small cell lung cancer, according to the American Society of Clinical Oncology.

Researchers at the CCIPD used AI to identify biomarkers from biopsy images for patients with NSCLC, as well as gynecologic cancers, that help predict the response to immunotherapy and clinical outcomes, including survival.

We have shown that the spatial interplay of features relating to the cancer nuclei and tumor-infiltrating lymphocytes drives a signal that allows us to identify which patients are going to respond to immunotherapy and which ones will not, said Anant Madabhushi, CCIPD director and Donnell Institute Professor of Biomedical Engineering at Case Western Reserve.

The study was published this month in the journal Science Advances.

Immunotherapy is expensive, and studies show that only 20-30% of patients respond to the treatment, according to National Institutes of Health and other sources. These findings validate that the AI technologies developed by the CCIPD can help clinicians determine how best to treat patients with NSCLC and gynecologic cancers, including cervical, endometrial and ovarian cancer, Madabhushi said.

The study, drawn from a retrospective analysis of data, also revealed new biomarker information regarding a protein called PD-L1 that helps prevent immune cells from attacking non-harmful cells in the body.

Patients with high PD-L1 often receive immunotherapy as part of their treatment for NSCLC, while patients with low PD-L1 are often not offered immunotherapy, or its coupled with chemotherapy.

Our work has identified a subset of patients with low PD-L1 who respond very well to immunotherapy and may not require immunotherapy plus chemotherapy, Madabhushi said. This could potentially help these patients avoid the toxicity associated with chemotherapy while also having a favorable response to immunotherapy.

The multi-site, multi-institutional study examined three common immunotherapy drugs (called checkpoint inhibitor agents) that target PD-L1: atezolizumab, nivolumab and pembrolizumab. The AI tools consistently predicted the response and clinical outcomes for all three immunotherapies.

The study is part of broader research conducted at CCIPD to develop and apply novel AI and machine-learning approaches to diagnose and predict the therapy response for various diseases and cancers, including breast, prostate, head and neck, brain, colorectal, gynecologic and skin.

The study coincides with Case Western Reserve recently signing a license agreement with Picture Health to commercialize AI tools to benefit patients with NSCLC and other cancers.

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Case Western Reserve University is one of the country's leading private research institutions. Located in Cleveland, we offer a unique combination of forward-thinking educational opportunities in an inspiring cultural setting. Our leading-edge faculty engage in teaching and research in a collaborative, hands-on environment. Our nationally recognized programs include arts and sciences, dental medicine, engineering, law, management, medicine, nursing and social work. About 5,800 undergraduate and 6,300 graduate students comprise our student body. Visitcase.eduto see how Case Western Reserve thinks beyond the possible.

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

1-Jun-2022

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Artificial intelligence tool predicts response to immunotherapy in lung and gynecologic cancer patients - EurekAlert

Nowadays the text we write is being processed by natural language processing models everywhere online. Whether its a social media platform like Twitter or Instagram, search engine, customer service chatbots or any other online service, text is being processed everywhere to train language models so that they could understand the users text more accurately and improve their experience.

Some common examples of how these models are working:

When you interact with the search engine, the model behind interprets words and phrases to understand the query then results that are relevant to your query are returned. Online retailers use NLP algorithms to determine which products are most likely to be of interest-based on the conversations people are having on social media platforms like Twitter or Instagram. Recommendation systems recommend books, movies, articles or any other thing based on what we read or what we write in comments and review.

The Arab world is a growing market. It is home to some of the fastest-growing economies in the world. And as the economies grow, so too does demand for services and products that cater to them including those reliant on accurate Arabic NLP capabilities.

Hassan Ghalib who is a Lead Data Scientist at Pandata Tech, a company focused on solving challenging problems and developing high-value-added solutions based on Big Data, Natural Language Processing (NLP), and Machine Learning, shared his thoughts about the challenges in Arabic NLP.

In the world of AI and machine learning data is the OIL. Good Performing models are trained on datasets that are huge in size and diverse in nature so that they cover all the aspects and richness of a language. Many novel architectures for language models such as The Transformer are only able to produce good metrics if they are trained on the right dataset. Because data quality along with quantity are the main driver of model performance," he said.

An accurate language model is one that is trained on unbiased datasets and is aware of the diversity and complexity of multiple dialects, vocabulary and grammar rules. Otherwise, if a language model is trained on dataset that lacks representation of certain Arab region its performance could be biased and could offend the cultural values and sentiments of people. For example, a model that predicts whether someone is likely to default on a loan could inadvertently discriminate against people from certain regions or religions if its trained on data that reflects only one perspective.

"If we talk about Arabic language, there are some challenges in Arabic NLP due to large number of dialects spoken throughout the Arab world where each dialect has its own unique vocabulary and grammar rules and insufficient datasets. The Arabic NLP models trained on such insufficient datasets result in being biased. If we look at state of the art language model available for other languages, the top of the list is GPT3, trained on hundreds of billions of tokens/words with the size of training dataset around 45 Terra bytes. If we have that much datasets for Arabic which are truly representative of all dialects spoken in different Arab regions then producing a GPT3 for the Arab world is not too far away, Ghalib added.

In this technological world machines are also learning just like humans, so the more data we give to the machines the more aware and accurate they will be. Qatar can avail this opportunity to produce massive datasets which can be harnessed to build top-notch Arabic NLP models. Doing this will not only preserve the language and values of Qatar in the future tech world but also, they will be pioneers in the region to reach such milestone.

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Pandata Tech scientist on the importance of Arabic data in the Future built around Artificial intelligence - The Peninsula

Blake Lemoine, an engineer at Google, has claimed that the firm's LaMDA artificial intelligence is sentient, but the expert consensus is that this is not the case

By Matthew Sparkes

Google is developing a range of artificial intelligence models

KENZO TRIBOUILLARD/AFP via Getty Images

A Google engineer has reportedly been placed on suspension from the company after claim that an artificial intelligence (AI) he helped to develop had become sentient. If I didnt know exactly what it was, which is this computer program we built recently, Id think it was a seven-year-old, eight-year-old kid, Blake Lemoine told the Washington Post.

Lemoine released transcripts of conversations with with the AI, called LaMDA (Language Model for Dialogue Applications), in which it appears to express fears of being switched off, talk about how it feels happy and sad, and attempts to form bonds with humans by talking about situations that it could never have actually experienced. Heres everything you need to know.

In a word, no, says Adrian Weller at the Alan Turing Institute.

LaMDA is an impressive model, its one of the most recent in a line of large language models that are trained with a lot of computing power and huge amounts of text data, but theyre not really sentient, he says. They do a sophisticated form of pattern matching to find text that best matches the query theyve been given thats based on all the data theyve been fed.

Adrian Hilton at the University of Surrey, UK agrees that sentience is a bold claim thats not backed up by the facts. Even noted cognitive scientist Steven Pinker weighed in to shoot down Lemoines claims, while Gary Marcus at New York University summed it up in one word: nonsense.

Neither Lemoine nor Google responded to New Scientists request for comment. But its certainly true that the output of AI models in recent years has become surprisingly, even shockingly good.

Our minds are susceptible to perceiving such ability especially when it comes to models designed to mimic human language as evidence of true intelligence. Not only can LaMDA make convincing chit-chat, but it can also present itself as having self-awareness and feelings.

As humans, were very good at anthropomorphising things, says Hilton. Putting our human values on things and and treating them as if they were sentient. We do this with cartoons, for instance, or with robots or with animals. We project our own emotions and sentience onto them. I would imagine thats whats happening happening in this case.

It remains unclear whether the current trajectory of AI research, where ever-larger models are fed ever-larger piles of training data, will see the genesis of an artificial mind.

I dont believe at the moment that we really understand the mechanisms behind what what makes something sentient and intelligent, says Hilton. Theres a lot of hype about AI, but Im not convinced that what were doing with machine learning, at the moment, is really intelligence in that sense.

Weller says that, given human emotions rely on sensory inputs, it might eventually be possible to replicate them artificially. It potentially, maybe one day, might be true, but most people would agree that theres a long way to go.

The Washington Post claims that Lemoine has been placed on suspension after seven years at Google, having attempted to hire a lawyer to represent LaMDA and sending executives a document that claimed the AI was sentient. Google also says that publishing the transcripts broke confidentiality policies.

Google told the Washington Post that: Our team, including ethicists and technologists, has reviewed Blakes concerns per our AI principles and have informed him that the evidence does not support his claims. He was told that there was no evidence that LaMDA was sentient (and lots of evidence against it).

Lemoine responded on Twitter: Google might call this sharing proprietary property. I call it sharing a discussion that I had with one of my coworkers.

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Has Google's LaMDA artificial intelligence really achieved sentience? - New Scientist

Dublin, June 13, 2022 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence (AI) Robots Market (2022-2027) by Offering, Robot, Technology, Deployment Mode, Application, Geography, Competitive Analysis, and the Impact of Covid-19 with Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence (AI) Robots Market is estimated to be USD 7.1 Bn in 2022 and is projected to reach USD 38.32 Bn by 2027, growing at a CAGR of 40.1%.

Market Dynamics

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

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

Company Profiles

The report provides a detailed analysis of the competitors in the market. It covers the financial performance analysis for the publicly listed companies in the market. The report also offers detailed information on the companies' recent development and competitive scenario. Some of the companies covered in this report are ABB Industries, F&P Personal Robotics, Hanson Robotics, Harman International, IBM, Intel, LG Electronics, Microsoft, Rethink Robotics, SoftbankXilinx,, etc.

Countries Studied

Competitive Quadrant

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

Ansoff Analysis

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

The report analyses the Global Artificial Intelligence (AI) Robots Market using the Ansoff Matrix to provide the best approaches a company can take to improve its market position.

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

Why buy this report?

Key Topics Covered:

1 Report Description

2 Research Methodology

3 Executive Summary

4 Market Dynamics4.1 Drivers4.1.1 High Adoption of Robots for Personal Use, Automation and mainly in Healthcare Industry4.1.2 Support to Such Robots from Governments Worldwide4.2 Restraints4.2.1 Absence of Standardized Regulations to Prevent Risks Associated With Networked and Autonomous Robots4.2.2 Reluctance to Adopt New Technologies4.3 Opportunities4.3.1 Focus on Developing Robots with Special Application Cases 4.3.2 Increasing Aging Population Worldwide Boosting the Demand for AI-based Robots for Elderly Assistance4.4 Challenges4.4.1 Developing Ai to Help Robots Make Better Decisions and Make Them Safe for Humans4.4.2 Long Time to Commercialize Robots and High Maintenance Cost

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

6 Global Artificial Intelligence (AI) Robots Market, By Offering6.1 Introduction6.2 Hardware 6.3 Software

7 Global Artificial Intelligence (AI) Robots Market, By Robot7.1 Introduction7.2 Service Robots7.2.1 Ground7.2.2 Aerial7.2.3 Underwater7.3 Industrial Robots7.3.1 Traditional Industrial Robots7.3.2 Collaborative Industrial Robots

8 Global Artificial Intelligence (AI) Robots Market, By Technology8.1 Introduction8.2 Computer Vision8.3 Context Awareness8.4 Machine Learning8.5 Natural Language Processing

9 Global Artificial Intelligence (AI) Robots Market, By Deployment Mode9.1 Introduction9.2 Cloud 9.3 On-Premise

10 Global Artificial Intelligence (AI) Robots Market, By Application10.1 Introduction10.2 Agriculture10.3 Education & Entertainment10.4 Healthcare Assistance10.5 Industrial10.6 Law Enforcement10.7 Military & Defence10.8 Personal Assistance & Care giving10.9 Public Relations10.10 Research & Space exploration10.11 Security & Surveillance10.12 Stock Management

11 Americas' Artificial Intelligence (AI) Robots Market11.1 Introduction11.2 Argentina11.3 Brazil11.4 Canada11.5 Chile11.6 Colombia11.7 Mexico11.8 Peru11.9 United States11.10 Rest of Americas

12 Europe's Artificial Intelligence (AI) Robots Market12.1 Introduction12.2 Austria12.3 Belgium12.4 Denmark12.5 Finland12.6 France12.7 Germany12.8 Italy12.9 Netherlands12.10 Norway12.11 Poland12.12 Russia12.13 Spain12.14 Sweden12.15 Switzerland12.16 United Kingdom12.17 Rest of Europe

13 Middle East and Africa's Artificial Intelligence (AI) Robots Market13.1 Introduction13.2 Egypt13.3 Israel13.4 Qatar13.5 Saudi Arabia13.6 South Africa13.7 United Arab Emirates13.8 Rest of MEA

14 APAC's Artificial Intelligence (AI) Robots Market14.1 Introduction14.2 Australia14.3 Bangladesh14.4 China14.5 India14.6 Indonesia14.7 Japan14.8 Malaysia14.9 Philippines14.10 Singapore14.11 South Korea14.12 Sri Lanka14.13 Thailand14.14 Taiwan14.15 Rest of Asia-Pacific

15 Competitive Landscape15.1 Competitive Quadrant15.2 Market Share Analysis15.3 Strategic Initiatives15.3.1 M&A and Investments15.3.2 Partnerships and Collaborations15.3.3 Product Developments and Improvements

16 Company Profiles 16.1 ABB Industries16.2 Alphabet16.3 Blue Frog Robotics16.4 Boston Dynamics16.5 Comau16.6 Diligent Robotics16.7 F&P Personal Robotics16.8 FANUC16.9 FRANKA EMIKA 16.10 Hanson Robotics 16.11 Harman International16.12 IBM 16.13 Intel 16.14 jibo16.15 KUKA16.16 LG Electronics 16.17 Microsoft 16.18 Neurala16.19 Pal Robotics 16.20 Promobot16.21 Rethink Robotics16.22 Softbank16.23 Staubli16.24 Xilinx

17 Appendix

For more information about this report visit https://www.researchandmarkets.com/r/p6baqx

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The Worldwide Artificial Intelligence (AI) Robots Industry is Expected to Reach $38.3 Billion by 2027 - GlobeNewswire

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

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

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

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

Competitive Quadrant

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

Ansoff Analysis

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

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

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

Why buy this report?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Market Segmentations

The Global Artificial Intelligence in Supply Chain Market is segmented based on Offering, Technology, Application, Industry, and Geography.

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/hx4h3q

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Global Artificial Intelligence in Supply Chain Market (2022 to 2027) - Growing Cloud-Based Applications Adoption Presents Opportunities -...