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Getafe players celebrate their first goal during the Spanish La Liga soccer match between FC ... [+] Barcelona and Getafe at the Camp Nou stadium in Barcelona, Spain, Thursday, April 22, 2021. Getafe is one of the teams that use Zone 7, an artificial intelligence platform to predict injury risks in players. (AP Photo/Joan Monfort)

For about two years, Tal Brown and Eyal Eliakim worked together at Salesforce.com Inc. CRM in the cloud-computing software companys Tel Aviv, Israel office. Brown was a product manager and Eliakim was a data analyst, with both men focusing on developing what would become Salesforces Einstein artificial intelligence system.

In 2017, Brown and Eliakim struck out on their own and formed Zone7, an artificial intelligence and machine learning platform focused on helping sports organizations predict the risk of injuries in players and improve their performance.

The company recently secured $8 million in a Series A funding round to help expand its reach in sports as well as other industries. Blumberg Capital, a San Francisco early-stage venture capital firm, led the round. Other investors included Resolute Ventures, UpWest, PLG Ventures and Joyance Partners, each of whom had also participated in Zone7s seed round through which the company raised $2.5 million in early 2019.

So far, Zone7 has worked with more 50 teams in numerous leagues in North America and Europe, including the English Premier League, Major League Soccer, National Football, La Liga, Serie A and Premiership Rugby.

Industries like sports whose success relies on healthy, high-performing individuals have only scratched the surface when it comes to using data to improve human performance, said Bruce Taragin, Blumberg Capitals managing director. Zone7 has the team and technology to take the data play in sports, among other industries, to a new level.

Zone7s system analyzes data that teams collect on athletes when theyre practicing, playing in games, working out and even sleeping. It then produces reports on each players fitness and other variables, forecasts their injury risk and suggests ways in which so-called operators such as coaches and medical personnel can help.

What we typically find is organizations already have a ton of data, Brown said. The need for a central brain to analyze all the data in context is really growing.

He added: Without Zone7, the job of the operator is really hard. Were like the cloud engine that will collect everything and analyze it in context.

Brown noted that some artificial intelligence systems such as those used in stock trading or on Amazon.com are intended to replace humans and eliminate any subjectivity when making decisions. But thats not the case with Zone7, according to Brown.

Were not creating an auto-pilot to get rid of coaches, he said. Thats exactly the opposite of what were trying to do. Were trying to put out the best instrumentation in front of the most elite coaches and others out there.

Zone7 plans on using the Series A funds to increase its staff from 10 to 25, hiring data scientists and engineers to improve the platform and sales and business development employees to complete deals with more organizations and leagues.

Although sports remains the focus of Zone7 and its largest vertical by far, the company is also looking to expand into other sectors, including health care, oil and gas companies and the military. Employees in those industries are also subject to injury risk or exhaustion and are valuable to their companies, so Brown envisions a future where employers and governments use Zone7 to make sure people are performing at their highest levels.

Brown, Zone7s chief executive, and Eliakim, its chief technology officer, are both former members of the Israel Defense Forces Intelligence Corps, where they worked on cybersecurity and big data projects. They now want to use their experience with big data, artificial intelligence and machine learning to help as many people as possible.

Im really passionate about this problem (of injuries and missed time) and the potential impact it can have on a persons well-being, Brown said. Sure, were dealing with a lot of athletes right now. But the potential of impacting a lot of others, whether its doctors or line workers or first responders, that to me is a personal motivation to work on this problem.

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Zone7 Raises $8 Million In Series A Round To Expand Artificial Intelligence Platform For Predicting Sports Injury Risks - Forbes

Global Artificial Intelligence (AI) Chips Market 2021-2025 The analyst has been monitoring the artificial intelligence (AI) chips market and it is poised to grow by $ 73. 49 billion during 2021-2025, progressing at a CAGR of over 51% during the forecast period.

New York, July 22, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence (AI) Chips Market 2021-2025" - https://www.reportlinker.com/p05006367/?utm_source=GNW Our report on the artificial intelligence (AI) chips market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.The report offers an up-to-date analysis regarding the current global market scenario, latest trends and drivers, and the overall market environment. The market is driven by the increasing adoption of AI chips in data centers, increased focus on developing AI chips for smartphones, and the development of AI chips in autonomous vehicles. In addition, the increasing adoption of AI chips in data centers is anticipated to boost the growth of the market as well.The artificial intelligence (AI) chips market analysis includes the product segment and geographic landscape.

The artificial intelligence (AI) chips market is segmented as below:By Product ASICs GPUs CPUs FPGAs

By Geography North America Europe APAC South America MEA

This study identifies the convergence of AI and IoT as one of the prime reasons driving the artificial intelligence (AI) chips market growth during the next few years. Also, increasing investments in ai start-ups and advances in the quantum computing market will lead to sizable demand in the market.

The analyst presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters. Our report on artificial intelligence (AI) chips market covers the following areas: Artificial intelligence (AI) chips market sizing Artificial intelligence (AI) chips market forecast Artificial intelligence (AI) chips market industry analysis

This robust vendor analysis is designed to help clients improve their market position, and in line with this, this report provides a detailed analysis of several leading artificial intelligence (AI) chips market vendors that include Alphabet Inc., Broadcom Inc., Intel Corp., NVIDIA Corp., Qualcomm Inc., Advanced Micro Devices Inc., Huawei Investment and Holding Co. Ltd., International Business Machines Corp., Samsung Electronics Co. Ltd., and Taiwan Semiconductor Manufacturing Co. Ltd. Also, the artificial intelligence (AI) chips market analysis report includes information on upcoming trends and challenges that will influence market growth. This is to help companies strategize and leverage all forthcoming growth opportunities.The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.

The analyst presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters such as profit, pricing, competition, and promotions. It presents various market facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive research - both primary and secondary. Technavios market research reports provide a complete competitive landscape and an in-depth vendor selection methodology and analysis using qualitative and quantitative research to forecast the accurate market growth.Read the full report: https://www.reportlinker.com/p05006367/?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 Global Artificial Intelligence (AI) Chips Market is expected to grow by $ 73.49 billion during 2021-2025, progressing at a CAGR of over 51% during...

DUBLIN, July 20, 2021 /PRNewswire/ -- The "Global Artificial Intelligence (AI) in Operating Room Market: Focus on Offering, Technology, Indication, Application, End User, Unmet Demand, Cost-Benefit Analysis, and Over 16 Countries' Data - Analysis and Forecast, 2021-2030" report has been added to ResearchAndMarkets.com's offering.

Global AI in Operating Room Market to Reach $2,951.5 Million by 2030

The purpose of the study is to enable the reader to gain a holistic view of the global AI in the operating room market by each of the aforementioned segments.

The report constitutes an in-depth analysis of the global AI in the operating room market, including a thorough analysis of the applications. The study also provides market and business-related information on various products, applications, technologies, and end users. The report considers software solutions and hardware solutions integrated with AI.

Expert Quote

"I think these are exciting times. Not considering the buzz around AI, ultimately, it is an enabler to do things at scale and quickly. It needs to serve a higher purpose that provides surgeons or other stakeholders in the healthcare ecosystem with value. The real value that a company provides with AI is the key component. This technology can be leveraged to tackle the disparity in the world of surgery".

Key Questions Answered in this Report:

In addition, the report provides:

Key Topics Covered:

1 Product Definition

2 Scope of Research

3 Research Methodology

4 Impact of COVID-19 on the Global AI in Operating Room Market4.1 Impact on Facilities4.2 Impact on AI Adoption in Operating Rooms4.3 Impact on Market Size4.4 COVID-19 Recovery Timeline4.5 Entry Barriers and Opportunities

5 Industry Analysis5.1 Technology Landscape5.1.1 Key Trends5.2 Value Chain Analysis5.3 Cost-Benefit Analysis5.4 End-User Perceptions5.5 Funding Scenario5.6 Regulatory Framework and Government Initiatives5.6.1 Regulations in North America5.6.1.1 U.S.5.6.1.1.1 Connected Devices5.6.1.1.2 Software-as-a-Medical Device (SaMD)5.6.1.1.2.1 General Considerations for SaMDs5.6.2 Regulations in Europe5.6.3 Regulations in Japan5.6.4 Regulations in China5.7 Patent Analysis5.7.1 Awaited Technological Developments5.7.2 Patent Filing Trend5.8 Product Benchmarking

6 Competitive Landscape6.1 Market Share Analysis6.2 Key Strategies and Developments6.3 Business Model Analysis6.4 Pricing Analysis6.5 Competitive Benchmarking

7 Global AI in Operating Room Market Scenario7.1 Assumptions and Limitations7.2 Global AI in Operating Room Market Assessment7.3 Key Findings and Opportunity Assessment7.4 Global AI in Operating Room Market Size and Forecast7.5 Market Dynamics7.5.1 Impact Analysis7.5.2 Market Growth Promoting Factors7.5.2.1 Growth in Funding for AI7.5.2.2 Growing Adoption of AI-Enabled Technologies in Healthcare Settings7.5.2.3 Advancement in Robotics and Medical Visualization Technologies7.5.2.4 Benefits of Artificial Intelligence-Enabled Surgeries Over Conventional Surgeries7.5.3 Market Growth Restraining Factors7.5.3.1 Lack of a Well-Defined Regulatory Framework in Regions7.5.3.2 Limited Studies and Data on the Efficiency of AI in Operating Rooms7.5.4 Market Growth Opportunities7.5.4.1 Leverage AI to Enhance Remote Surgical Capabilities7.5.4.2 Leveraging Business Synergies for Capability and Portfolio Enhancement7.5.5 Current Surgical Challenges7.5.6 Capitalizing on Unmet Demand

8 Global AI in Operating Room Market (by Offering)8.1 Key Findings and Opportunity Assessment8.2 Hardware8.3 Software-as-a-Service (SaaS)

9 Global AI in Operating Room Market (by Technology)9.1 Key Findings and Opportunity Assessment9.2 Machine Learning (ML) and Deep Learning9.3 Natural Language Processing (NLP)

10 Global AI in Operating Room Market (by Indication)10.1 Key Findings and Opportunity Assessment10.2 Cardiology10.3 Orthopedics10.4 Urology10.5 Gastroenterology10.6 Neurology

11 Global AI in Operating Room Market (by Application)11.1 Key Findings and Opportunity Assessment11.2 Training11.3 Diagnosis11.4 Surgical Planning and Rehabilitation11.4.1 Pre-Operative11.4.2 Intra-Operative11.4.3 Post-Operative11.5 Outcomes and Risk Analysis11.6 Integration and Connectivity11.7 Others (Instrument Tracking and Traceability, Scheduling, Anesthesia Management)

12 Global AI in Operating Room Market (by End User)12.1 Opportunity Assessment12.2 Hospitals12.3 Others (Ambulatory Surgical Centers, Private, Standalone, and Specialized Facilities)

13 Global AI in Operating Room Market (by Region)

14 Case Studies14.1 Enabling the Future Operating Room with AI14.2 Role of M&As in the Future of AI in Operating Room14.3 Role of AI in Minimally Invasive Surgeries

15 Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/555a1

Media Contact: Research and Markets Laura Wood, Senior Manager [emailprotected]

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SOURCE Research and Markets

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Global Operating Room Artificial Intelligence (AI) Market Report 2021-2030 with Case Studies to Assess the Key Strategies Adopted by Some of the...

"We reported results of the first longitudinal approach to natural language processing of unstructured medical notes and demonstrate its ability to update and improve a prognostic model over time, as a patient's oncologic illness course unfolds," said Olivier Morin, leader of the project and head of the physics division at UCSF. The work was pioneered at UCSF, and is the result of a collaboration among an international consortium comprising U.S., (UCSF, San Francisco) Canadian (McGill, Universit de Sherbrooke, Montreal) and European centers (Oncoray in Dresden and Maastricht University).

Catherine Park, M.D. Co-senior author and Chair of the department of Radiation Oncology at UCSF added: "With this data we were able to validate findings of published clinical trials using real world data, e.g. the positive impact of immunotherapy in lung cancer. In addition, there are exciting opportunities to generate hypothesis based on associations from patients' individual health profiles, and risk factors."

Professor Phillippe Lambin senior author and Chair of the department of Precision Medicine at Maastricht University adds, "The MEDomics infrastructure allowed us to validate several new clinical hypotheses like the importance of cardio-vascular morbidity for the outcome of cancer treatment."

The consortium has created a secure, dynamic, continuously learning and expandable infrastructure, termed MEDomics, designed to constantly capture multimodal electronic health information, including imaging, across a large and multicentric healthcare system (watch the animation: https://youtu.be/2030Pdgm3_4).

Dr. Morin and collaborators added, now part of the code is open source and we would like to expand the international consortium (visitwww.medomics.ai).Our vision is to create an open-source computation platform integrating all MEDomics developments and from which both clinical staff and research scientists could tackle a diverse range of oncological problems using AI.

Contact: Dr. Olivier Morin PhD, [emailprotected], +14153089257

SOURCE University of California - San Francisco

ucsf.edu

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"Promising development in the fight with cancer using artificial intelligence" with a new privacy-preserving approach named MEDomics -...

Can you imagine a just and equitable world where everyone, regardless of age, gender or class, has access to excellent healthcare, nutritious food and other basic human needs? Are data-driven technologies such as artificial intelligence and data science capable of achieving this or will the bias that already drives real-world outcomes eventually overtake the digital world, too?

Bias represents injustice against a person or a group. A lot of existing human bias can be transferred to machines because technologies are not neutral; they are only as good, or bad, as the people who develop them. To explain how bias can lead to prejudices, injustices and inequality in corporate organizations around the world, I will highlight two real-world examples where bias in artificial intelligence was identified and the ethical risk mitigated.

In 2014, a team of software engineers at Amazon were building a program to review the resumes of job applicants. Unfortunately, in 2015 they realized that the system discriminated against women for technical roles. Amazon recruiters did not use the software to evaluate candidates because of these discrimination and fairness issues. Meanwhile in 2019, San Francisco legislators voted against the use of facial recognition, believing they were prone to errors when used on people with dark skin or women.

The National Institute of Standards and Technology (NIST) conducted research that evaluated facial-recognition algorithms from around 100 developers from 189 organizations, including Toshiba, Intel and Microsoft. Speaking about the alarming conclusions, one of the authors, Patrick Grother, says: "While it is usually incorrect to make statements across algorithms, we found empirical evidence for the existence of demographic differentials in the majority of the algorithms we studied.

Ridding AI and machine learning of bias involves taking their many uses into consideration

Image: British Medical Journal

To list some of the source of fairness and non-discrimination risks in the use of artificial intelligence, these include: implicit bias, sampling bias, temporal bias, over-fitting to training data, and edge cases and outliers.

Implicit bias is discrimination or prejudice against a person or group that is unconscious to the person with the bias. It is dangerous because the person is unaware of the bias whether it be on grounds of gender, race, disability, sexuality or class.

This is a statistical problem where random data selected from the population do not reflect the distribution of the population. The sample data may be skewed towards some subset of the group.

This is based on our perception of time. We can build a machine-learning model that works well at this time, but fails in the future because we didn't factor in possible future changes when building the model.

This happens when the AI model can accurately predict values from the training dataset but cannot predict new data accurately. The model adheres too much to the training dataset and does not generalize to a larger population.

These are data outside the boundaries of the training dataset. Outliers are data points outside the normal distribution of the data. Errors and noise are classified as edge cases: Errors are missing or incorrect values in the dataset; noise is data that negatively impacts on the machine learning process.

Analytical techniques require meticulous assessment of the training data for sampling bias and unequal representations of groups in the training data. You can investigate the source and characteristics of the dataset. Check the data for balance. For instance, is one gender or race represented more than the other? Is the size of the data large enough for training? Are some groups ignored?

A recent study on mortgage loans revealed that the predictive models used for granting or rejecting loans are not accurate for minorities. Scott Nelson, a researcher at the University of Chicago, and Laura Blattner, a Stanford University economist, found out that the reason for the variance between mortgage approval for majority and minority group is because low-income and minority groups have less data documented in their credit histories. Without strong analytical study of the data, the cause of the bias will be undetected and unknown.

What if the environment you trained the data is not suitable for a wider population? Expose your model to varying environments and contexts for new insights. You want to be sure that your model can generalize to a wider set of scenarios.

A review of a healthcare-based risk prediction algorithm that was used on about 200 million American citizens showed racial bias. The algorithm predicts patients that should be given extra medical care. It was found out that the system favoured white patients over black patients. The problem with the algorithms development is that it wasn't properly tested with all major races before deployment.

Inclusive design emphasizes inclusion in the design process. The AI product should be designed with consideration for diverse groups such as gender, race, class, and culture. Foreseeability is about predicting the impact the AI system will have right now and over time.

Recent research published by the Journal of the American Medical Association (JAMA) reviewed more than 70 academic publications based on the diagnostic prowess of doctors against digital doppelgangers across several areas of clinical medicine. A lot of the data used in training the algorithms came from only three states: Massachusetts, California and New York. Will the algorithm generalize well to a wider population?

A lot of researchers are worried about algorithms for skin-cancer detection. Most of them do not perform well in detecting skin cancer for darker skin because they were trained primarily on light-skinned individuals. The developers of the skin-cancer detection model didn't apply principles of inclusive design in the development of their models.

Testing is an important part of building a new product or service. User testing in this case refers to getting representatives from the diverse groups that will be using your AI product to test it before it is released.

This is a method of performing strategic analysis of external environments. It is an acronym for social (i.e. societal attitudes, culture and demographics), technological, economic (ie interest, growth and inflation rate), environmental, political and values. Performing a STEEPV analysis will help you detect fairness and non-discrimination risks in practice.

The COVID-19 pandemic and recent social and political unrest have created a profound sense of urgency for companies to actively work to tackle inequity.

The Forum's work on Diversity, Equality, Inclusion and Social Justice is driven by the New Economy and Society Platform, which is focused on building prosperous, inclusive and just economies and societies. In addition to its work on economic growth, revival and transformation, work, wages and job creation, and education, skills and learning, the Platform takes an integrated and holistic approach to diversity, equity, inclusion and social justice, and aims to tackle exclusion, bias and discrimination related to race, gender, ability, sexual orientation and all other forms of human diversity.

The Platform produces data, standards and insights, such as the Global Gender Gap Report and the Diversity, Equity and Inclusion 4.0 Toolkit, and drives or supports action initiatives, such as Partnering for Racial Justice in Business, The Valuable 500 Closing the Disability Inclusion Gap, Hardwiring Gender Parity in the Future of Work, Closing the Gender Gap Country Accelerators, the Partnership for Global LGBTI Equality, the Community of Chief Diversity and Inclusion Officers and the Global Future Council on Equity and Social Justice.

It is very easy for the existing bias in our society to be transferred to algorithms. We see discrimination against race and gender easily perpetrated in machine learning. There is an urgent need for corporate organizations to be more proactive in ensuring fairness and non-discrimination as they leverage AI to improve productivity and performance. One possible solution is by having an AI ethicist in your development team to detect and mitigate ethical risks early in your project before investing lots of time and money.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Research shows AI is often biased. Here's how to make algorithms work for all of us - World Economic Forum