Artificial Intelligence has arrived and it is good time that the C-suite especially the CEOs take a note of it. However, with the media hype surrounding digital transformation and AI the decision-makers of an enterprise often left in quandary as to how and when to implement AI and what to do with this business transformative technology.

With tangible results and takeaways, AI has shown real outcomes for early adopters resulting in a sense of trust and a feeling of assurance. To aid the C-Suite to derive benefits from this technology, Harvard Business Review has come up with a set of pointers which enterprisers both big and small need to know, for AI success in their workplace-

1. C-suite must take its time to evaluate the critical success from AI before deciding on a pilot.

2. It is good to believe in the hype surrounding AI implementation, disruptive technologies can potentially boost enterprise returns.

3. AI transformation might not succeed without the support of decision-making management.

4. Partnering for capability and capacity creation is a must for AI success.

5. Trust other technologies too, and avoid the temptation of putting tech teams solely in charge of AI implementation.

6. Accelerate the enterprise AI journey with a portfolio approach.

7. Machine Learning is powerful, but weigh your enterprise use cases before selecting the technology.

8. Build digital capabilities before an AI pilot project.

9. Take the change in overseeing the AI pilot in the first place.

10. Beware, people, change management and process-up-gradation are the biggest challenges.

The buzz around Artificial Intelligence (AI) has grown by leaps and bounds, all set to instil confidence among the C-suite all across the world. This is marked by an increase in investments and the widespread interest by venture capitalists, tech powerheads and change-makers. AI-infused digital transformation success stories are becoming all the louder and more prominent across enterprises crisscrossing domain functionalities.

The key adoption point of the IA influx arises from the adoption in AI machine learning and NLP infusion, to deliver more output and results that suits all the AI adopters. The AI adaptability across different industries will be different from BFSI, Telecom and Logistics all set to lead the way, while healthcare and the government sector is slowly and steadily preparing for the transitional shift.

In the future, developing new business models to build a growth path that is flexible and robust will be critical to digitization. The same seems to hold for AI, early AI adopters have been very proactive and robust in adoption g to the change, setting up examples for others to follow.

Summing up, the C-Suite must not make any mistake, the digital adoption is here, and the faster they realize its presence and embrace to these new technologies, the quicker they would adopt and stay in the competition race, else the time will come soon for perish. We are talking of a technology-dominated digital transformation era in some years from now.

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Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

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How do CEO's Succeed with Artificial Intelligence at their Workplace? - Analytics Insight

A PwC study in 2017 estimated the world would gain $15.7 trillion by 2030 if artificial intelligence (AI) was adopted across nations. The study said that AI would first lead to productivity enhancement, and a major portion of gains would accrue from consumer-side effects. China, it had said, could see its GDP rising by around a fourth as it was using AI more aggressively. Although the study did not estimate how much India would gain from using AI, new research by Icrier along with Nasscom and Google shows that even a marginal increase in artificial intelligence adoption may add 2.5% to GDP in the immediate term. Moreover, it highlights that if the government spends the Rs 7,000 crore it had envisaged for the national AI programme, GDP could get boosted by as much as $86 billion. The way Icrier sees it, as AI becomes what it calls a general purpose technologylike the internetits impact rises; essentially, then, the pace of Indias digitisation drive will determine how fast AI is adopted.

To understand how fast the adoption of AI can take place and its impact on total factor productivity, Icrier studied 1,553 firms that have some software investment. What it found was that there was a huge gap in the use of AI, suggesting a large untapped potential. AI-intensity was defined as the ratio of software investment to total sales, and the study found that, for instance, in the case of agriculture, while the average AI intensity is 0.001, the maximum intensity was 20 times as much. For electrical and optical equipment manufacturing, the difference between the average and the top in the industry was 145-times; it was 742 in the case of trade and in the case of services, the average intensity was 0.159, while the maximum intensity was 110.

The report, however, argues that businesses alone wont be able to push AI, the government will have to play a bigger role, by setting up a nodal AI agency to push for AI-adoption and also drive business, government and academia partnerships. Another suggestion is to initiate large-scale skill development programmes to get the workforce ready for AI-adoption. What is worrying, however, is the slow pace of digital adoption so far, though the pandemic has helped speed up things a big; both the education and health sector, for instance, are likely to see faster adoption of AI techniques. A related problem is that of cybersecurity where India needs both a national strategy and a governance structure that is more well-defined.

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The AI-boost: Using more artificial intelligence will boost GDP growth - The Financial Express

C3.ai Digital Transformation Institute awards $5.4 million to top researchers to steer how society responds to the pandemic.

Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.

Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the worlds leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.

The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.

The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.

We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics, says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.

Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mdry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays.

Bertsimass project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglars research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahlehs research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes.

The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapidstart and significant progress of the C3.ai Digital Transformation Institute, says Thomas Siebel, chair and CEO of C3.ai. It is a privilege to be working with such an accomplished team.

The following projects are the MIT recipients of the inaugural C3.ai DTI Awards:

Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science

Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)

Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics

Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering

Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections David Gifford, professor of biological engineering and of electrical engineering and computer science

Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19 Aleksander Mdry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)

Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor

Continued here:
MIT Using Artificial Intelligence to Help Put an End to the COVID-19 Pandemic - SciTechDaily

But knowing what a difference their support can make (home court advantage, anyone?) the NBA is proposing a few solutions: a tap-to-cheer app and video technology that will teleport their faces court-side from the comfort of their homes.

"It's obviously very different for the players and it's different for the fans watching at home. I mean, in this sport -- like a lot of others -- there's that home court advantage, that six-man. It's the roar of the crowd, the boos of the crowd," said NBA commissioner Adam Silver Wednesday on CNN with Wolf Blitzer. "We are trying to replicate that to a certain extent without piping in obvious crowd noise."

It's still unclear what kind of difference this technology will make in the overall atmosphere of a sporting match, though.

Not every attempt has been successful, though.

In South Korea, FC Seoul was fined 100 million Korean won (around $81,000) after being accused of placing sex dolls in its stands to add to the atmosphere during a closed match.

CNN's Jack Guy contributed to this report.

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The NBA will use artificial intelligence and a tap-to-cheer app feature to help fans stuck at home get in the game - CNN

DUBLIN, July 30, 2020 /PRNewswire/ -- The "Artificial Intelligence in Retail Market by Product, Application (Predictive Merchandizing, Programmatic Advertising), Technology (Machine Learning, Natural Language Processing), Deployment (Cloud, On-Premises), and Geography - Global Forecast to 2027" report has been added to ResearchAndMarkets.com's offering.

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The global artificial intelligence in retail market is expected to grow at a CAGR of 34.4% from 2020 to reach $19.9 billion by 2027.

The growth in the artificial intelligence in retail market is driven by several factors such as the rising number of internet users, increasing adoption of smart devices, rapid adoption of advances in technology across retail chain, and increasing adoption of the multi-channel or omnichannel retailing strategy.

Besides, the factors such as increasing awareness about AI and big data & analytics, consistent proliferation of Internet of Things, and enhanced end-user experience is also contributing to the market growth. However, the high cost of transformation and lack of infrastructure are some of the major factors hindering the market growth during the forecast period.

The study offers a comprehensive analysis of the global artificial intelligence in retail market with respect to various types. The global artificial intelligence in retail market study presents historical market data (2018 & 2019), estimated current data (2020), and forecasts for 2027. The market is segmented on the basis of product, application, technology, retail, end-user, and geography.

Based on product offering, the solutions segment is estimated to command the largest share of the overall artificial intelligence in retail market in 2020. This is attributed to the growing adoption of AI-powered solutions and applications by retailers across the globe to identify personalized customer needs, reduce shrinkage by improving loss prevention at point-of-sale, and enhance customer engagement experience. However, the services segment is estimated to witness rapid growth during the forecast period.

In AI solutions segment, based on product type, the chatbots segment is estimated to command the largest share of the artificial intelligence in retail solutions market in 2020. The large share of this segment is mainly attributed to the growing need to improve customer relationship management (CRM) and an increase in awareness about the advantages offered by chatbots over other customer support options. However, customer behavior tracking is poised to post the fastest growth during the forecast period.

Based on learning technology,the machine learning segment is estimated to command the largest share of the overall artificial intelligence in retail market in 2020. The large share of this segment is mainly attributed to the increasing demand from retailers to track dynamic consumer behavior in order to ensure competitive edge in the retail industry, which has also proved as a key to success of stakeholders in many cases. Moreover, ability of machine learning technology to provide better prediction of sales and customer services, better segmentation of customers, and high personalized product recommendations for advertising and promotions is expected to drive the adoption of machine learning technology during the forecast period.An in-depth analysis of the geographic scenario of the market provides detailed qualitative and quantitative insights about the five regions including North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. In 2020, North America region is estimated to command the largest share of the global artificial intelligence in retail market, followed by Europe and Asia Pacific. The large share of this region is mainly attributed to its open-minded approach towards smart technologies and high technology adoption rate, presence of key players & start-ups, and increased internet access. However, the factors such as speedy growth in spending power, presence of young population, and government initiatives supporting digitalization is helping Asia Pacific to register the fastest growth in the global artificial intelligence in retail market.

Key players operating in the global artificial intelligence in retail market are Amazon.com, Inc. (U.S.), Google LLC (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), SAP SE (Germany), Salesforce.com, Inc. (U.S.), and BloomReach, Inc. (U.S.) along with several local and regional players.

Story continues

Key Topics Covered

1. Introduction

2. Research Methodology

3. Executive Summary

4. Market Insights 4.1. Introduction 4.2. Market Dynamics 4.2.1. Drivers 4.2.1.1. Growing Awareness about AI and Big Data & Analytics 4.2.1.2. Adoption of Multichannel or Omni channel Retailing Strategy 4.2.1.3. Need to Enhance the End-User Experience and Improve Productivity 4.2.2. Restraints 4.2.2.1. High Cost of Procurement 4.2.2.2. Lack of infrastructure 4.2.3. Opportunities 4.2.3.1. Increased Adoption of AI-Powered Voice Enabled Devices 4.2.3.2. Growing Number of Smartphones 4.2.4. Challenges 4.2.4.1. Concerns over Privacy and Identity of individuals 4.2.4.2. Lack of Awareness about AI Technology 4.2.5. Trends 4.2.5.1. Rising Focus on Blockchain 4.2.5.1. Adoption of 5G Technology 4.3. Impact of COVID-19 on the AI in Retail Market

5. Artificial Intelligence in Retail Market, by Product Type 5.1. Introduction 5.2. Solutions 5.2.1. Chatbot 5.2.2. Recommendation Engines 5.2.3. Customer Behavior Tracking 5.2.4. Visual Search 5.2.5. Customer Relationship Management 5.2.6. Price Optimization 5.2.7. Supply Chain Management 5.2.8. inventory Management 5.3. Services 5.3.1. Managed Services 5.3.2. Professional Services

6. Artificial Intelligence in Retail Market, by Application 6.1. Introduction 6.2. Predictive Merchandising 6.3. Programmatic Advertising 6.4. In-Store Visual Monitoring & Surveillance 6.5. Market forecasting 6.6. Location-Based Marketing

7. Artificial Intelligence in Retail Market, by Learning Technology 7.1. Introduction 7.2. Machine Learning 7.3. Natural Language Processing 7.4. Computer Vision

8. Artificial Intelligence in Retail Market, by Type 8.1. Introduction 8.2. Online Retail 8.3. Offline Retail 8.3.1. Brick & Mortar Stores 8.3.2. Supermarkets& Hypermarkets 8.3.3. Speciality Stores

9. Artificial Intelligence in Retail Market, by End-User 9.1. Introduction 9.2. Food &Groceries 9.3. Health & Wellness 9.4. Automotive 9.5. Electronics & White Goods 9.6. Fashion & Clothing 9.7. Others

10. Artificial Intelligence in Retail Market, by Deployment Type 10.1. Introduction 10.2. Cloud 10.3. On-Premise

11. Global Artificial Intelligence in Retail Market, by Geography 11.1. Introduction 11.2. North America 11.2.1. U.S. 11.2.2. Canada 11.3. Europe 11.3.1. Germany 11.3.2. France 11.3.3. U.K. 11.3.4. Italy 11.3.5. Spain 11.3.6. Rest of Europe 11.4. Asia-Pacific 11.4.1. Japan 11.4.2. China 11.4.3. India 11.4.4. Rest of the Asia-Pacific 11.5. Latin America 11.6. Middle East & Africa

12. Competitive Landscape 12.1. Competitive Growth Strategies 12.1.1. New Product Launches 12.1.2. Mergers and Acquisitions 12.1.3. Partnerships, Agreements, and Collaborations 12.1.4. Expansions 12.2. Market Share Analysis 12.3. Competitive Benchmarking

13. Company Profiles (Business Overview, Financial Overview, Product Portfolio, Strategic Developments)13.1. Amazon 13.2. Google LLC 13.3. IBM Corporation 13.4. Intel Corporation 13.5. Microsoft Corporation 13.6. Nvidia Corporation 13.7. Oracle Corporation 13.8. SAP SE 13.9. Bloomreach, Inc. 13.10. Salesforce.com, Inc.

For more information about this report visit https://www.researchandmarkets.com/r/3cmz4u

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$19.9B Artificial Intelligence in Retail Industry, 2027 - Rising Focus on Blockchain and Adoption of 5G Technology - Yahoo Finance

AI experts with Windfall Geotek will use their advanced Computer Aided Resource Detection System to mark targets using pattern recognition and machine learning

Inc ()(OTCQX:GATGF) announced Thursday it will employ artificial intelligence (AI) to identify possible gold targets at the Larder gold project in Ontario.

The company said AI experts with Windfall Geotek will use their advanced Computer Aided Resource Detection System (CARDS) to marktargets which will be evaluated and explored using traditional exploration techniques in upcoming programs.

Gatling's Larder Gold project occupies 3,370 hectares along the Cadillac Larder Lake Break, a prolific structural gold trend. The property hosts three high-grade deposits along the main break, as well as two additional, underexplored gold trends, recently discovered 6 kilometers north.

The company said AI uses pattern recognition and machine learning to make predictions based on compiled datasets. The Larder project benefits from a vast database of recent and historical data, including 2,000 drill holes, 90,000 assays, 1,000 surface rock samples, 500 soil samples, as well as geophysics, Lidar and bedrock geology.

The area to be analyzed has numerous deposits including, but not limited to, Agnico Eagle's Upper Beaver, Kerr Addison, Mistango River Resources' Omega Mine and Gatling's 3 high-grade gold deposits: Fernland, Cheminis and Bear,Gatling said in a statement.

These known gold deposits will be instrumental in guiding the AI, with the goal of highlighting areas on Larder that may be geologically similar to other deposits in the district.

Contact the author: [emailprotected]

Follow him on Twitter @PatrickMGraham

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Gatling Exploration to use artificial intelligence to identify possible gold targets at the Larder project in Ontario - Proactive Investors USA &...

Artificial Intelligence acts as a potential key technology of dystopian future concepts, social control, and autocratic world power fantasies. It is gradually finding its way to the public and private board room discussions and government policies. Even countries like Germany, which were lagging in the AI race, have gone through tremendous change in the past few years. According to PwC research, by 2030, Germany alone shall have Gross Domestic Product (GDP) up by 11.3% and generate 430 billion due to AI. And by percentage, this potential is more than most of the other European Nations. This makes the country as Europes largest economy, with a thriving market and high potential for new to market brands. The study also that industries like healthcare, energy, and the auto industry will benefit from significant productivity gains by adopting AI applications.

While Germany is currently at the forefront of AI in Europe, research and innovative projects have also commenced in the Cyber Valley. The goal is to further the mission to develop increasingly sophisticated machines with extensive capabilities and boost R&D in AI. Founded just four years ago by the Max Planck Institute for Intelligent Systems (MPII) together with auto groups Bosch, Daimler, BMW, and Porsche, the cluster had also secured 1.25 million investment from Amazon for research partnership. The main motive behind this initiative is to leverage AI to make theGerman industries, services, and products even better. Germany is also striving to bring the digital revolution through Industry 4.0, which was also mentioned in the AI strategy of 2018. The strategy report further expresses that the country shall expand its strong position and rise to be a global leader in AI on the grounds of ethics and legal terms too. It also intends to use AI to promote social participation, freedom of action, and self-determination for citizens and foster the sustainable development of the society. To achieve this goal, the Federal Government first allocated a total of 500 million to beef up the AI strategy for 2019 and further anticipates matching funds from the private sector and other the federal states, therefore bringing the total investment to 6 billion.

Meanwhile, the emphasis is also made on improving data sharing facilities by providing open access to governmental data. The government is also working to build a reliable data and analysis infrastructure based on cloud platforms and upgraded storage and computing capacity. These measures are crucial and necessary as, without data, AI innovations cannot be used to solve the bottlenecks and other issues faced by different industries in their quest for AI adoption. Recently, Germany is looking for ways to tighten data security. It is calling for a more concrete definition when data records must be stored on a mandatory basis. At the European Union, it has also requested for developing a new classification scheme together with the member states.

On the business front, tests are carried to maximize the use of collaborative AI robots and link augmented reality technology to AI-based production planning systems. Major automobile behemoths Volkswagen, BMW, and Daimler, are investing heavily in modern, AI-controlled factories. They are working on solutions for assisted and autonomous driving, intelligent operating systems, entertainment systems, and navigation systems at their German R&D centers.

Germany is also growing as a preferred hub for startups focusing on AI and its applications like machine learning, deep learning, computer vision, predictive analysis, and so on. Further, it has the most active corporate venture investors in Europe (91% of all non-IPO exitsin 2019 were related to corporates). The most common areas of focus for these AI startups are software development, image recognition, customer support and communication, and marketing and sales. These five categories are found to constitute around 48% of German AI startups. Currently, Berlin is the fourth largest global AI hub, following Silicon Valley of the USA, London, and Paris. So, it is high time that companies take Germany as an upcoming nation in global AI leadership and start investing or collaborating with it.

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A Brief Outlook on the Artificial Intelligence landscape in Germany - Analytics Insight

On July 8, 2020, the House Financial Services Committees Taskforce on Artificial Intelligence held a hearing entitled Exposure Notification and Contact Tracing: How AI Helps Localities Reopen Safely and Researchers Find a Cure.

In his opening remarks, Congressman Bill Foster (D-IL), chairman of the task force, stated that the hearing would discuss the essential tradeoffs that the coronavirus disease 2019 (COVID-19) pandemic was forcing on the public between life, liberty, privacy and the pursuit of happiness. Chairman Foster noted that what he called invasive artificial intelligence (AI) surveillance may save lives, but would come at a tremendous cost to personal liberty. He said that contact tracing apps that use back-end AI, which combines raw data collected from voluntarily participating COVID-19-positive patients, may adequately address privacy concerns while still capturing similar health and economic benefits as more intrusive monitoring.

Congressman Barry Loudermilk (R-GA) discussed how digital contact tracing could be more effective than manual contact tracing, but noted that it must have strong participation from people 40-60 percent adoption rate overall to be effective. He said that citizens would need to trust that their privacy would not be violated. To help establish this trust, he suggested, people would need to be able to easily determine what data would be collected, who would have access to the data and how the data would be used.

Four panelists testified at this hearing. Below is a summary of each panelists testimony, followed by an overview of some of the post-testimony questions that committee members raised:

Brian McClendon, the CEO and co-founder of the CVKey Project, discussed how privacy, disclosure and opt-in data collection impact the ability to identify and isolate those infected with COVID-19. AI and machine learning require large amounts of data. He stated that while the most valuable data to combat COVID-19 can be found in the contact-tracing interviews of infected and exposed people, difficulties exist in capturing this information. For example, attempted phone calls to reach exposed individuals may go unanswered because people often do not pick up calls from unknown numbers. Mobile apps, he said, offer a way to conduct contact tracing with greater accuracy and coverage. Mr. McClendon discussed two ways that such apps could work: (1) using GPS location or (2) via low-energy Bluetooth. For the latter, Mr. McClendon explained a method developed by two large technology companies: when a user of a digital contact tracing app tests positive for COVID-19, he or she then chooses to opt in to upload non-personally identifiable information to a state-run cloud server, which would then determine whether potential exposures have occurred and provide in-app notifications to such users.

Krutika Kuppalli, M.D., an infectious diseases physician, discussed how using contact tracing can help impede the spread of infectious diseases. She noted that it is important to remember ethical considerations involving public health information, data protection and data privacy when using these technologies.

Andre M. Perry, a fellow at the Brookings Institution, began his presentation by discussing how COVID-19 has disproportionately affected Black and Latino populations, reflecting historical inequalities and structural racism. Mr. Perry identified particular concerns regarding AI and contact tracing as they pertain to structural racism and bias. These tools, he stated, are not neutral and can either exacerbate or mitigate structural racism. To address such bias, he suggested, contact tracing should include people who have generally been excluded from systems that have provided better health and economic outcomes. Further, the use of AI tools in the healthcare arena presents the same risk as in other fields: the AI is only as good as the programmers who design it. Bias in programming can lead to flaws in technology and amplify biases in the real world. Mr. Perry stated that greater recruitment and investment with Black-owned tech firms, rigorous reviews and testing for bias and more engagement with local communities is required.

Ramesh Raskar, a professor at MIT and the founder of the PathCheck Foundation, emphasized three elements during his presentation: (1) how to augment manual contact tracing with apps; (2) how to make sure apps are privacy-preserving, inclusive, trustworthy, and built using open-source methods and nonprofits; and (3) the creation of a National Pandemic Response Service. Regarding inclusivity, Mr. Raskar noted that Congress should actively require that solutions be accessible broadly and generally; contact tracing cannot be effective only for segments of the population that have access to the latest technology.

Post-testimony questions

Chairman Foster asked about limits of privacy-preserving techniques by providing an example of a person who had been isolated for a week, then interacted with only one other person, and then later received a notification of exposure: such a person likely will know the identity of the infected person. Mr. Raskar replied that data protection has different layers: confidentiality, anonymity, and then privacy. In public health scenarios, Mr. Raskar stated that today, we only care about confidentiality and not anonymity or privacy (eventually, he commented, you will have to meet a doctor).

If we were to implement a federal contact tracing program, Representative Loudermilk asked, how would we ensure citizens that they can know what data will be used and collected, and who has access? Mr. McClendon responded that under the approach developed by the two large technology companies, data is random and stored on a personal phone until the user opts in to upload random numbers to the server. The notification determination is made on the phone and the state provides the messages. The state will not know who the exposed person is until that person opts in by calling the manual contact tracing team.

Representative Maxine Waters (D-CA) asked what developers of a mobile contact tracing technology should consider to ensure that minority communities are not further disadvantaged. Mr. Perry reiterated that AI technologies have not been tested, created, or vetted by persons of color, which has led to various biases.

Congressman Sean Casten (D-IL) asked whether AI used in contact tracing is solely backward-looking or could predict future hotspots. Mr. McClendon replied that to predict the future, you need to know the past. Manual contact tracing interviews, where an infected or exposed person describes where he or she has been, would provide significant data to include in a machine-learning algorithm, enabling tracers to predict where a hotspot might occur in the future. However, privacy issues and technological incompatibility (e.g., county and state tools that are not compatible with each other) mean that a lot of data is currently siloed and even inaccessible, impeding the ability for AI to look forward.

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Task Force on Artificial Intelligence - hearing to discuss use of AI in contact tracing - Lexology

What is artificial intelligence and why is it so famous?

Artificial intelligence is the talk of the town. It is the simulation of human intelligence with the usage of machines and especially the management of the computer system. AI can be categorized in a lot of streams. This means that their primary basis of categorization is dependent on the weakness and how strong they can be. We all know that the application of Artificial intelligence is increasing in this modern world, and each and every technology is managing their resources in the right way. Take, for example, apples voice control uses their Artificial intelligence known as Siri to communicate and get your work done in the best of form.

How is it changing the current scenario?

Here is the list of features and advantages of using Artificial intelligence.

Units of Artificial Intelligence

These are the following units of AI which work for the current period.

All these units of artificial intelligence have different features of their own. These units are fundamental in your life, and they help to paint the whole world. AI is the new simulation of the human, which allows you to process data and include the techniques of learning. We need AI for the work we do. It becomes an automated routine for us to use their units for our daily work. Like take, for example, the usage of robotics is increasing, and it is said to cross a massive platform in a few years. Even though it is a sub-field, it holds as much crucial as the central concept. And if you are interested then you can choose one field and excel in the same.

Does the work for you

Artificial intelligence is changing the current scenario in the way you have never seen before. The smallest of activities are being conducted by them. They dont need to take breaks like us. If you work regularly, then your body might give up on you, but Artificial intelligence wont ever do the same. They are programmed to work for a very long period. They dont need lunch breaks, and neither can they ever get tired. You need to recharge their cells so that they dont shut off.

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Researchers from Stanford have created an AI-powered bot to automatically cover up the faces of Black Lives Matter protesters in photos.

Everyone should have the right to protest. And, if done legally, to do so without fear of having things like their future job prospects ruined because theyve been snapped at a demonstration from which a select few may have gone on to do criminal acts such as arson and looting.

With images from the protests being widely shared on social media to raise awareness, police have been using the opportunity to add the people featured within them to facial recognition databases.

Over the past weeks, we have seen an increasing number of arrests at BLM protests, with images circulating around the web enabling automatic identification of those individuals and subsequent arrests to hamper protest activity, the researchers explain.

Software has been available for some time to blur faces, but recent AI advancements have proved that its possible to deblur such images.

Researchers from Stanford Machine Learning set out to develop an automated tool which prevents the real identity of those in an image from being revealed.

The result of their work is BLMPrivacyBot:

Rather than blur the faces, the bot automatically covers them up with the black fist emoji which has become synonymous with the Black Lives Matter movement. The researchers hope such a solution will be built-in to social media platforms, but admit its unlikely.

The researchers trained the model for their AI bot on a dataset consisting of around 1.2 million people called QNRF. However, they warn its not foolproof as an individual could be identified through other means such as what clothing theyre wearing.

To use the BLMPrivacyBot, you can either send an image to its Twitter handle or upload a photo to the web interface here. The open source repo is available if you want to look at the inner workings.

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Tags: ai, artificial intelligence, black lives matter, blm, bot, face recognition, facial recognition, privacy, protest, surveillance

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Researchers create AI bot to protect the identities of BLM protesters - AI News