The India Meteorological Department (IMD) is planning to use artificial intelligence in weather forecasting, especially for issuing nowcasts, which can help improve 3-6 hours prediction of extreme weather events, its Director General Mrutunjay Mohapatra said on Sunday.

He said the use of artificial intelligence and machine learning is not as prevalent as it is in other fields and it is relatively new in the area of weather forecasting.The IMD has invited research groups who can study how artificial intelligence (AI) be used for improving weather forecasting and the Ministry of Earth Sciences is evaluating their proposals, Mohapatra said.

He said the IMD is also planning to do collaborative studies on this with other institutions. The IMD uses different tools like radars, satellite imagery, to issue nowcasts, which gives information on extreme weather events occurring in the next 3-6 hours.

The IMD issues forecasts for extreme weather events like thunderstorms, dust storms. Unlike cyclones, predictions of thunderstorms, which also bring lightning, squall and heavy rains, are more difficult as the extreme weather events develop and dissipate in a very short period of time.

Last month, over 160 people died due to lightning alone in Uttar Pradesh and Bihar. The IMD wants to better the nowcast predictions through AI and machine learning. Artificial intelligence helps in understanding past weather models and this can make decision-making faster, Mohapatra said.

The National Oceanic and Atmospheric Administration (NOAA) of the US announced new strategies this year to expand the agencys application of four emerging science and technology focus areas NOAA Unmanned Systems, artificial intelligence, Omics, and the cloud to guide transformative advancements in the quality and timeliness of NOAA science, products and services.

Omics is a suite of advanced methods used to analyse material such as DNA, RNA, or proteins. With regards to AI, it said the overarching goal of the NOAA Artificial Intelligence (AI) Strategy is to utilise AI to advance NOAAs requirements-driven mission priorities.

The NOAA said through this,?it seeks to reduce the cost of data processing, and provide higher quality and more timely scientific products and services for societal benefits.

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IMD plans to use artificial intelligence in weather forecasting - The Financial Express

Following COVID-19, the use of AI in both public and private sectors seems unavoidable. Many industries stand to benefit greatly from its use; for example; in the manufacturing industry (e.g. industrial robots), in transport (e.g. autonomous vehicles), in financial markets, health and medical care (e.g. medical robots, diagnostic tools and assistive technology), as well as more generally, for example, for self-cleaning public places. However, there are risks related to AI, including its opacity or, as often referred to, its black box features. European Institutions have been trying to address this, and the related issues for a number of years, but the spotlight, post-COVID-19, is now firmly focused on AI.

The European Commission has drafted many documents and including a White Paper on Artificial Intelligence (19 February 2020), with a subsequent draft report detailing recommendations to the Commission on a civil liability regime for AI. This document also suggested a motion calling for a European Parliament Resolution and drawing up a European Parliament and Council Regulation on liability relating to the operation of AI-systems (27 April 2020).

A further study was commissioned by the Policy Department C, at the request of the Committee on Legal Affairs. This study on Artificial Intelligence and Civil Liability was published on 14 July 2020.

In all of these documents, the European Institutions and expert groups stress that a key issue arising from the use of AI (in public or private sectors) is the liability for potential damages, in relation to the use of, or defects caused by AI tools. Many AI-systems depend on external data and are vulnerable to cybersecurity breaches. With opacity and increased autonomy in AI, it becomes increasingly difficult to identify the liable party and the harmed individual, making it challenging to obtain compensation.

Currently, the Product Liability Directive (85/374/CEE) is the framework governing such liability. This directive has been implemented in national member states and it places liability on the producer for damages caused by a product defect. The consumer and generally the injured person has to show evidence of the causal link between the defect of the product and the damage. In a case of damages caused by an AI tool, this, is not so easy to prove.

Nonetheless, these experts stress that a complete review of the general European legal framework on civil liability is not required, but it is necessary to adapt the legislation in force and introduce new provisions.

In light of this, the draft report of the European Parliament includes a proposal for a regulation of the European Parliament and of the Council on liability for the operation of AI-systems. This regulation, if approved, would introduce a new form of liability for the party deploying the AI-system - defined as the person who decides on the use of AI-systems, exercises control over the associated risks and benefits from its operation.

Notably, the proposed regulation provides for a strict liability for high-risk AI-systems, these are systems that display intelligent behavior (see art. 3 and 4). In line with other legislation regarding civil liability in critical and high-risks sectors, the proposed regulation provides for a compulsory insurance cover. Additionally, the proposed regulation establishes the maximum amount of compensation damages.

By contrast, according to art. 8 of the proposed regulation, the deployer of an AI-system not defined as a high-risk AI-system in accordance to the provisions of the regulation, shall be subjected to fault-based liability for any harm or damage caused by a physical or virtual activity, device or process driven by the AI-system.

In short, this means a double track of liability based on the risk of the activity.

Reflecting the traditional principles of civil liability, the proposed regulation introduces other provisions regarding; damages, limitation period, multiple tortfeasors, and so on. In line with other documents that address the issue of liability, the proposed new regulation attempts to find a balance between the protection of user rights and collectivity, and the creation of new and innovative technologies.

Finally, as technology changes faster than legislation in many cases, even the newest legislation may not cover every challenge posed by AI. In the interim, general rules and principles in force should be applied in every legal system, as the law continues to change and adapt.

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Artificial Intelligence and civil liability. Who pays the damages? - Lexology

New York, Aug. 01, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Conversational Artificial Intelligence Market Research Report by Operations, by Product, by Technology, by Type, by Industry, by Deployment - Global Forecast to 2025 - Cumulative Impact of COVID-19" - https://www.reportlinker.com/p05913968/?utm_source=GNW

The Global Conversational Artificial Intelligence Market is expected to grow from USD 4,290.05 Million in 2019 to USD 17,142.57 Million by the end of 2025 at a Compound Annual Growth Rate (CAGR) of 25.97%.

Market Segmentation & Coverage:This research report categorizes the Conversational Artificial Intelligence to forecast the revenues and analyze the trends in each of the following sub-markets:

Based on Operations, the Conversational Artificial Intelligence Market studied across Branding & Advertisement, Customer Engagement and Retention, Customer Support, Data Privacy & Compliance, Onboarding & Employee Engagement, and Personal Assistant.

Based on Product, the Conversational Artificial Intelligence Market studied across Platform and Services. The Services further studied across Consulting Services, Managed Services, Professional Services, Support & Maintenance, and Training & Education.

Based on Technology, the Conversational Artificial Intelligence Market studied across Automated Speech Recognition, Machine Learning and Deep Learning, and Natural Language Processing.

Based on Type, the Conversational Artificial Intelligence Market studied across Chatbots and Intelligent Virtual Assistants.

Based on Industry, the Conversational Artificial Intelligence Market studied across Aerospace & Defense, Automotive & Transportation, Banking, Financial Services & Insurance, Building, Construction & Real Estate, Consumer Goods & Retail, Education, Energy & Utilities, Government & Public Sector, Healthcare & Life Sciences, Information Technology, Manufacturing, Media & Entertainment, Telecommunication, and Travel & Hospitality.

Based on Deployment, the Conversational Artificial Intelligence Market studied across On-Cloud and On-Premises.

Based on Geography, the Conversational Artificial Intelligence Market studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas region surveyed across Argentina, Brazil, Canada, Mexico, and United States. The Asia-Pacific region surveyed across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, South Korea, and Thailand. The Europe, Middle East & Africa region surveyed across France, Germany, Italy, Netherlands, Qatar, Russia, Saudi Arabia, South Africa, Spain, United Arab Emirates, and United Kingdom.

Company Usability Profiles:The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Conversational Artificial Intelligence Market including Amazon Web Services, Inc., Artificial Solutions, Avaamo, Inc., Baidu, Inc., Cognigy Inc., Conversica, Inc., Google Inc., Haptik Infotech Pvt Ltd., International Business Machines Corporation, Microsoft Corporation, Nuance Communications, Inc., Oracle Corporation, Rasa Technologies GmbH, Rulai, Inc., and SAP SE.

FPNV Positioning Matrix:The FPNV Positioning Matrix evaluates and categorizes the vendors in the Conversational Artificial Intelligence Market on the basis of Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Competitive Strategic Window:The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth.

Cumulative Impact of COVID-19:COVID-19 is an incomparable global public health emergency that has affected almost every industry, so for and, the long-term effects projected to impact the industry growth during the forecast period. Our ongoing research amplifies our research framework to ensure the inclusion of underlaying COVID-19 issues and potential paths forward. The report is delivering insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The updated study provides insights, analysis, estimations, and forecast, considering the COVID-19 impact on the market.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on the market offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments

The report answers questions such as:1. What is the market size and forecast of the Global Conversational Artificial Intelligence Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Conversational Artificial Intelligence Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Conversational Artificial Intelligence Market?4. What is the competitive strategic window for opportunities in the Global Conversational Artificial Intelligence Market?5. What are the technology trends and regulatory frameworks in the Global Conversational Artificial Intelligence Market?6. What are the modes and strategic moves considered suitable for entering the Global Conversational Artificial Intelligence Market?Read the full report: https://www.reportlinker.com/p05913968/?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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Conversational Artificial Intelligence Market Research Report by Operations, by Product, by Technology, by Type, by Industry, by Deployment - Global...

This article was originally published here

Can Assoc Radiol J. 2020 Jul 31:846537120941434. doi: 10.1177/0846537120941434. Online ahead of print.

ABSTRACT

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)-based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

PMID:32735493 | DOI:10.1177/0846537120941434

Continued here:
Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making - DocWire News

AI applications in healthcare can literally change patients lives, improving diagnostics and treatment and helping patients and healthcare providers make informed decisions quickly.

AI in the global healthcare market (the total value of products and services sold) was valued at $2.4 billion in 2019 and is projected to reach $31.02 billion in 2025.

Now in the COVID-19 pandemic, AI is being leveraged to identify virus-related misinformation on social media and remove it. AI is also helping scientists expedite vaccine development, track the virusand understand individual and population risk, among other applications.

Companies such as Microsoft, which recently stated it will dedicate $20 million to advance the use of artificial intelligence in COVID-19 research, recognize the need for and extraordinary potential of AI in healthcare.

The ultimate goal of AI in healthcare is to improve patient outcomes by revolutionizing treatment techniques. By analyzing complex medical data and drawing conclusions without direct human input, AI technology can help researchers make new discoveries.

Various subtypes of AI are used in healthcare. Natural language processing algorithms give machines the ability to understand and interpret human language. Machine learning algorithms teach computers to find patterns and make predictions based on massive amounts of complex data.

AI is already playing a huge role in healthcare, and its potential future applications are game-changing. Weve outlined four distinct ways that AI is transforming the healthcare industry.

This transformative technology has the ability to improve diagnostics, advance treatment options, boost patient adherence and engagement, and support administrative and operational efficiency.

AI can help healthcare professionals diagnose patients by analyzing symptoms, suggesting personalized treatments and predicting risk. It can also detect abnormal results.

Analyzing symptoms, suggesting personalized treatments and predicting risk

Many healthcare providers and organizations are already using intelligent symptom checkers. This machine learning technology asks patients a series of questions about their symptoms and, based on their answers, informs them of appropriate next steps for seeking care.

Buoy Health offers a web-based, AI-powered health assistant that healthcare organizations are using to triage patients who have symptoms of COVID-19. It offers personalized information and recommendations based on the latest guidance from the Centers for Disease Control and Prevention.

Additionally, AI can take precision medicine healthcare tailored to the individual to the next level by synthesizing information and drawing conclusions, allowing for more informed and personalized treatment. Deep learning models have the ability to analyze massive amounts of data, including information about a patients genetic content, other molecular/cellular analysis and lifestyle factors and find relevant research that can help doctors select treatments.

AI can also be used to develop algorithms that make individual and population health risk predictions in order to help improve outcomes. At the University of Pennsylvania, doctors used a machine learning algorithm that can monitor hundreds of key variables in real time to anticipate sepsis or septic shock in patients 12 hours before onset.

Detecting disease

Imaging tools can advance the diagnostic process for clinicians. The San Francisco-based company Enlitic develops deep learning medical tools to improve radiology diagnoses by analyzing medical data. These tools allow clinicians to better understand and define the aggressiveness of cancers. In some cases, these tools can replace the need for tissue samples with virtual biopsies, which would aid clinicians in identifying the phenotypes and genetic properties of tumors.

These imaging tools have also been shown to make more accurate conclusions than clinicians. A 2017 study published in JAMA found that of 32 deep learning algorithms, seven were able to diagnose lymph node metastases in women with breast cancer more accurately than a panel of 11 pathologists.

Smartphones and other portable devices may also become powerful diagnostic tools that could benefit the areas of dermatology and ophthalmology. The use of AI in dermatology focuses on analyzing and classifying images and the ability to differentiate between benign and malignant skin lesions.

Using smartphones to collect and share images could widen the capabilities of telehealth. In ophthalmology, the medical device company Remidio has been able to detect diabetic retinopathy using a smartphone-based fundus camera, a low-power microscope with an attached camera.

AI is becoming a valuable tool for treating patients. Brain-computer interfaces could help restore the ability to speak and move in patients who have lost these abilities. This technology could also improve the quality of life for patients with ALS, strokes, or spinal cord injuries.

There is potential for machine learning algorithms to advance the use of immunotherapy, to which currently only 20% of patients respond. New technology may be able to determine new options for targeting therapies to an individuals unique genetic makeup. Companies like BioXcel Therapeutics are working to develop new therapies using AI and machine learning.

Additionally, clinical decision support systems can help assist healthcare professionals make better decisions by analyzing past, current and new patient data. IBM offers clinical support tools to help healthcare providers make more informed and evidence-based decisions.

Finally, AI has the potential to expedite drug development by reducing the time and cost for discovery. AI supports data-driven decision making, helping researchers understand what compounds should be further explored.

Wearables and personalized medical devices, such as smartwatches and activity trackers, can help patients and clinicians monitor health. They can also contribute to research on population health factors by collecting and analyzing data about individuals.

These devices can also be useful in helping patients adhere to treatment recommendations. Patient adherence to treatment plans can be a factor in determining outcome. When patients are noncompliant and fail to adjust their behaviors or take prescribed drugs as recommended, the care plan can fail.

The ability of AI to personalize treatment could help patients stay more involved and engaged in their care. AI tools can be used to send patients alerts or content intended to provoke action. Companies like Livongo are working to give users personalized health nudges through notifications that promote decisions supporting both mental and physical health.

AI can be used to create a patient self-service model an online portal accessible by portable devices that is more convenient and offers more choice. A self-service model helps providers reduce costs and helps consumers access the care they need in an efficient way.

AI can improve administrative and operational workflow in the healthcare system by automating some of the process. Recording notes and reviewing medical records in electronic health records takes up 34% to 55% of physicians time, making it one of the leading causes of lost productivity for physicians.

Clinical documentation tools that use natural language processing can help reduce the time providers spend on documentation time for clinicians and give them more time to focus on delivering top-quality care.

Health insurance companies can also benefit from AI technology. The current process of evaluating claims is quite time-consuming, since 80% of healthcare claims are flagged by insurers as incorrect or fraudulent. Natural language processing tools can help insurers detect issues in seconds, rather than days or months.

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How AI is revolutionizing healthcare - Nurse.com

This year is likely to be remembered for the Covid-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.

The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point.

That may sound unglamorous, but a language model built for guessing with 175 billion parameters 10 times more than previous competitors is surprisingly powerful.

The eventual uses of GPT-3 are hard to predict, but it is easy to see the potential. GPT-3 can converse at a conceptual level, translate language, answer email, perform (some) programming tasks, help with medical diagnoses and, perhaps someday, serve as a therapist. It can write poetry, dialogue and stories with a surprising degree of sophistication, and it is generally good at common sense a typical failing for many automated response systems. You can even ask it questions about God.

Imagine a Siri-like voice-activated assistant that actually did your intended bidding. It also has the potential to outperform Google for many search queries, which could give rise to a highly profitable company.

GPT-3 does not try to pass the Turing test by being indistinguishable from a human in its responses. Rather, it is built for generality and depth, even though that means it will serve up bad answers to many queries, at least in its current state. As a general philosophical principle, it accepts that being weird sometimes is a necessary part of being smart. In any case, like so many other technologies, GPT-3 has the potential to rapidly improve.

It is not difficult to imagine a wide variety of GPT-3 spinoffs, or companies built around auxiliary services, or industry task forces to improve the less accurate aspects of GPT-3. Unlike some innovations, it could conceivably generate an entire ecosystem.

There is a notable buzz about GPT-3 in the tech community. One user in the UK tweeted: I just got access to gpt-3 and I can't stop smiling, i am so excited. Venture capitalist Paul Graham noted coyly: Hackers are fascinated by GPT-3. To everyone else it seems a toy. Pattern seem familiar to anyone? Venture capitalist and AI expert Daniel Gross referred to GPT-3 as a landmark moment in the field of AI.

I am not a tech person, so there is plenty about GPT-3 I do not understand. Still, reading even a bit about it fills me with thoughts of the many possible uses.

It is noteworthy that GPT-3 came from OpenAI rather than from one of the more dominant tech companies, such as Alphabet/Google, Facebook or Amazon. It is sometimes suggested that the very largest companies have too much market power but in this case, a relatively young and less capitalized upstart is leading the way. (OpenAI was founded only in late 2015 and is run by Sam Altman).

GPT-3 is also a sign of the underlying health and dynamism of the Bay Area tech world, and thus of the US economy. The innovation came to the US before China and reflects the power of decentralized institutions.

Like all innovations, GPT-3 involves some dangers. For instance, if prompted by descriptive ethnic or racial words, it can come up with unappetizing responses. One can also imagine that a more advanced version of GPT-3 would be a powerful surveillance engine for written text and transcribed conversations. Furthermore, it is not an obvious plus if you can train your software to impersonate you over email. Imagine a world where you never know who you are really talking to Is this a verified email conversation? Still, the hope is that protective mechanisms can at least limit some of these problems.

We have not quite entered the era where Skynet goes live, to cite the famous movie phrase about an AI taking over (and destroying) the world. But artificial intelligence does seem to have taken a major leap forward. In an otherwise grim year, this is a welcome and hopeful development. Oh, and if you would like to read more, here is an article about GPT-3 written by GPT-3.

Bloomberg

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Artificial Intelligence Is the Hope 2020 Needs - Asharq Al-awsat - English

New carbon emission tracking technology will quantify emissions of greenhouse gas, holding the energy industry accountable for its CO2 output. Backed by Google, this cutting-edge initiative will be known as Climate TRACE (Tracking Real-Time Atmospheric Carbon Emissions).

Advanced AI and machine learning now make it possible to trace greenhouse gas (GHG) emissions from factories, power plants and more. By using image processing algorithms to detect carbon emissions from power plants, AI technology makes use of the growing global satellite network to develop a more comprehensive global database of power plant activity. Because most countries self-report emissions and manually compile results, scientists often rely on data that is several years out of date. Moreover, companies often underreport carbon emissions, rendering existing data inaccurate.

Climate TRACE addresses these issues by partnering with other leaders in sustainability practicesincluding former U.S. Vice President Al Gore, WattTime, CarbonPlan, Carbon Tracker, Earthrise Alliance, Hudson Carbon, OceanMind, Rocky Mountain Institute, Blue Sky Analytics and Hypervine. The Climate TRACE coalition aims to help countries in meeting Paris Agreement targets and place the world on a path to sustainability.

The carbon tracking efforts of Climate TRACE will result in a conglomeration of data to be made available to the public, which may assist plaintiffs in climate liability cases and lead to enhanced enforcement of environmental laws. The slow pace of international climate negotiations has led to an increase in lawsuits demanding action on global warming. As of this year, 1,600 climate-related lawsuits have been filed worldwide, including 1,200 lawsuits in the United States alone. Currently, climate liability cases rely predominantly on a database run by the Carbon Disclosure Project and the Climate Accountability Institute. This database, initially released in 2013 as the Carbon Majors Report, attempts to link carbon pollution to emitters. The 2013 report pinpointed 100 producers responsible for 71% of global industrial GHG emissions. Its 2017 report, for instance, indicated that 25 corporate and state producing entities account for 51% of global industrial GHG emissions. While the Carbon Majors Report has assisted in determining the largest carbon emitters on a global scale, Climate TRACE will provide more frequent and accurate monitoring of pollutants.

Data from Climate TRACE will also help hold countries accountable to the Paris Climate Agreement, expanding upon European efforts to monitor global warming. Early last year, a space budget increase put Europe in the lead to monitor carbon from space using satellite technology. In December 2019, member governments awarded the European Space Agency $12.5 billion. This substantial increase allowed the ESA to devote $1.8 billion to Copernicus, a satellite technology program which continuously tracks Earths atmosphere. The program allowed Europe to analyze human carbon emissions regularly. With Copernicus, the ESA became the only space agency to monitor pledges made under the Paris Climate Agreement. The Climate TRACE coalitionwith members spanning across three continentswill make carbon monitoring a global effort.

Climate TRACE has created a working prototype that is currently in its developmental stages. The coalition intends to release its first version of the AI project by the summer of 2021.

[View source.]

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Artificial Intelligence and Satellite Technology to Enhance Carbon Tracking Measures - JD Supra

The Role

Applications are invited for a PDRA post in Artificial Intelligence.

1. Work closely with multi-national consumer goods corporation to identify short-list of applications of Artificial Intelligence (AI) in data mining, image processing, knowledge gathering etc. and to identify a short-list of projects that would benefit from AI methods.

2. For projects on the short-list, to deeper dive into the projects in order to define deliverables, what data is needed, milestones, etc. Deliverables could include a finished working model, successful proof of principle and a clear path forward, or a detailed assessment of why the proof of principle was not successful together with recommendations on how to address the problem in the future.

3. Over the course of the project duration, to undertake at least three R&D projects at Durham and to present a monthly updates to the relevant project teams at the corporation.

4. To hold a final workshop session at the corporation summarising and presenting the R&D work.

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Postdoctoral Research Associate in Artificial Intelligence job with DURHAM UNIVERSITY | 215559 - Times Higher Education (THE)

While there's plenty to be critical about when it comes to retiring U.S. Rep. Will Hurd his records on the environment and health care, for example it's a fair bet at least some of his constituents will miss his bipartisanship.

After all, the San Antonio-area Republican co-wonAlleghenyCollege's 2018Prize for Civility in Public Life for his 30-hour "bipartisan road trip" with Beto O'Rourke, back when when the latter was just another Texas congressman and not yet a Democratic superstar.

Apparently, even in the waning months of his term, Hurd has kept up that spirit of reaching across the aisle.

The former CIA intelligence officer recentlyworked with U.S. Rep. Robin Kelly, D-Illinois, to author a detailed report on how to keep the U.S. from falling behind China on artificial intelligence. That's important, the pair argue, because AI has big implications for defense and national security.

Among the two House members' suggestions: getting the federal government to devote more money to deploying safe AI and cutting off Chinas access to AI-specific microchips.

The techie bible Wired Magazine was impressed enough with the pair's work that it devoted some serious real estate to letting them delve into their plan. Turns out Hurd and Kelly are alsodrafting a congressional resolution on their AI concerns and plan to introduce similar legislation.

Some of that I hope we get done in this Congress, and others can be taken and run with in the next Congress, Hurd told the mag.

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San Antonio GOP Congressman Will Hurd Reaches Across the Aisle on Artificial Intelligence - San Antonio Current

AI applied to tumor microscopy images detects patterns of 167 different mutations and predicts patient survival in 28 cancer types

Researchers at EMBLs European Bioinformatics Institute (EMBL-EBI), the Wellcome Sanger Institute, Addenbrookes Hospital in Cambridge, UK, and collaborators have developed an artificial intelligence (AI) algorithm that uses computer vision to analyze tissue samples from cancer patients. They have shown that the algorithm can distinguish between healthy and cancerous tissues, and can also identify patterns of more than 160 DNA and thousands of RNA changes in tumors. The study, published in Nature Cancer, highlights the potential of AI for improving cancer diagnosis, prognosis, and treatment.

Cancer diagnosis and prognosis are largely based on two main approaches. In one, histopathologists examine the appearance of cancer tissue under the microscope. In the other, cancer geneticists, analyze the changes that occur in the genetic code of cancer cells. Both approaches are essential to understand and treat cancer, but they are rarely used together.

Clinicians use microscopy slides for cancer diagnosis all the time. However, the full potential of these slides hasnt been unlocked yet. As computer vision advances, we can analyze digital images of these slides to understand what happens at a molecular level, says Yu Fu, Postdoctoral Fellow in the Gerstung Group at EMBL-EBI.

Computer vision algorithms are a form of artificial intelligence that can recognize certain features in images. Fu and colleagues repurposed such an algorithm developed by Google originally used to classify everyday objects such as lemons, sunglasses and radiators to distinguish various cancer types from healthy tissue. They showed that this algorithm can also be used to predict survival and even patterns of DNA and RNA changes from images of tumor tissue.

Teaching algorithms to detect molecular changes

Previous studies have used similar methods to analyze images from single or a few cancer types with selected molecular alterations. However, Fu and colleagues generalized the approach on an unprecedented scale: they trained the algorithm with more than 17 000 images from 28 cancer types collected for The Cancer Genome Atlas, and studied all known genomic alterations.

What is quite remarkable is that our algorithm can automatically link the histological appearance of almost any tumor with a very broad set of molecular characteristics, and with patient survival, explains Moritz Gerstung, Group Leader at EMBL-EBI.

Overall, their algorithm was capable of detecting patterns of 167 different mutations and thousands of gene activity changes. These findings show in detail how genetic mutations alter the appearance of tumor cells and tissues.

Another research group has independently validated these results with a similar AI algorithm applied to images from eight cancer types. Their study was published in the same issue of Nature Cancer.

A potential tool for personalized medicine

The integration of molecular and histopathological data provides a clearer picture of a tumors profile. Detecting the molecular features, cell composition, and survival associated with individual tumors would help clinicians tailor appropriate treatments to their patients needs.

From a clinicians point of view, these findings are incredibly exciting. Our work shows how artificial intelligence could be used in clinical practice, explains Luiza Moore, Clinician Scientist and Pathologist at the Wellcome Sanger Institute and Addenbrookes Hospital. While the number of cancer cases is increasing worldwide, the number of pathologists is declining. At the same time, we strive to move away from the one size fits all approach and into personalized medicine. A combination of digital pathology and artificial intelligence can potentially alleviate those pressures and enhance our practice and patient care.

Sequencing technologies have propelled genomics to the forefront of cancer research, yet these technologies remain inaccessible to most clinics around the world. A possible alternative to direct sequencing would be to use AI to emulate a genomic analysis using data that is cheaper to collect, like microscopy slides.

Getting all that information from standard tumor images in a completely automatic manner is revolutionary, says Alexander Jung, PhD student at EMBL-EBI. This study shows what might be possible in the coming years, but these algorithms will have to be refined before clinical implementation.

Source article:

FU, Y., et al. (2020). Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis, Nature Cancer. Published online 27 07; DOI: 10.1038/s43018-020-0085-8

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Industry News: Artificial intelligence finds patterns of mutations and survival in tumor images - SelectScience