Category Archives: Artificial Intelligence

As far as a simple definition, Artificial Intelligence is the ability of a machine or a computer device to imitate human intelligence (cognitive process), secure from experiences, adapt to the most recent data and work people-like-exercises.

Artificial Intelligence executes tasks intelligently that yield in creating enormous accuracy, flexibility, and productivity for the entire system. Tech chiefs are looking for some approaches to implement artificial intelligence technologies into their organizations to draw obstruction and include values, for example, AI is immovably utilized in the banking and media industry. There is a wide arrangement of methods that come in the space of artificial intelligence, for example, linguistics, bias, vision, robotics, planning, natural language processing, decision science, etc. Let us learn about some of the major subfields of AI in depth.

ML is maybe the most applicable subset of AI to the average enterprise today. As clarified in the Executives manual for real-world AI, our recent research report directed by Harvard Business Review Analytic Services, ML is a mature innovation that has been around for quite a long time.

ML is a part of AI that enables computers to self-learn from information and apply that learning without human intercession. When confronting a circumstance wherein a solution is covered up in a huge data set, AI is a go-to. ML exceeds expectations at processing that information, extracting patterns from it in a small amount of the time a human would take and delivering in any case out of reach knowledge, says Ingo Mierswa, founder and president of the data science platform RapidMiner. ML powers risk analysis, fraud detection, and portfolio management in financial services; GPS-based predictions in travel and targeted marketing campaigns, to list a few examples.

Joining cognitive science and machines to perform tasks, the neural network is a part of artificial intelligence that utilizes nervous system science ( a piece of biology that worries the nerve and nervous system of the human cerebrum). Imitating the human mind where the human brain contains an unbounded number of neurons and to code brain-neurons into a system or a machine is the thing that the neural network functions.

Neural network and machine learning combinedly tackle numerous intricate tasks effortlessly while a large number of these tasks can be automated. NLTK is your sacred goal library that is utilized in NLP. Ace all the modules in it and youll be a professional text analyzer instantly. Other Python libraries include pandas, NumPy, text blob, matplotlib, wordcloud.

An explainer article by AI software organization Pathmind offers a valuable analogy: Think of a lot of Russian dolls settled within one another. Profound learning is a subset of machine learning and machine learning is a subset of AI, which is an umbrella term for any computer program that accomplishes something smart.

Deep learning utilizes alleged neural systems, which learn from processing the labeled information provided during training and uses this answer key to realize what attributes of the information are expected to build the right yield, as per one clarification given by deep AI. When an adequate number of models have been processed, the neural network can start to process new, inconspicuous sources of info and effectively return precise outcomes.

Deep learning powers product and content recommendations for Amazon and Netflix. It works in the background of Googles voice-and image-recognition algorithms. Its ability to break down a lot of high-dimensional information makes deep learning unmistakably appropriate for supercharging preventive maintenance frameworks

This has risen as an extremely sizzling field of artificial intelligence. A fascinating field of innovative work for the most part focuses around designing and developing robots. Robotics is an interdisciplinary field of science and engineering consolidated with mechanical engineering, electrical engineering, computer science, and numerous others. It decides the designing, producing, operating, and use of robots. It manages computer systems for their control, intelligent results and data change.

Robots are deployed regularly for directing tasks that may be difficult for people to perform consistently. Major robotics tasks included an assembly line for automobile manufacturing, for moving large objects in space by NASA. Artificial intelligence scientists are additionally creating robots utilizing machine learning to set interaction at social levels.

Have you taken a stab at learning another language by labeling the items in your home with the local language and translated words? It is by all accounts a successful vocab developer since you see the words again and again. Same is the situation with computers fueled with computer vision. They learn by labeling or classifying various objects that they go over and handle the implications or decipher, however, at a much quicker pace than people (like those robots in science fiction motion pictures).

The tool OpenCV empowers processing of pictures by applying them to mathematical operations. Recall that elective subject in engineering days called Fluffy Logic? Truly, that approach is utilized in Image processing that makes it a lot simpler for computer vision specialists to fuzzify or obscure the readings that cant be placed in a crisp Yes/No or True/False classification. OpenTLA is utilized for video tracking which is the procedure to find a moving object(s) utilizing a camera video stream.

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Five Important Subsets of Artificial Intelligence - Analytics Insight

The national flags of the U.S. and China waving outside a building.

Teh Eng Koon | AFP via Getty Images

The United States might be leading in some areas of its technology race with China but experts warn against the world's largest economy resting on its laurels, urging instead for cooperation with allies and shifts in domestic policy.

Alongside trade war developments between the U.S. and China, both parties have been embroiled in growing competition to dominate various fields of next-generation technology, such as 5G networks and artificial intelligence (AI).

5G refers to the latest mobile networking technology that promises super-fast download speeds and the ability to underpin critical infrastructure. That's one reason why it is seen as crucial technology for both countries.

In the last few years, Beijing has laid out a number of plans it hopes will turn China into a world leader in various tech areas:

U.S.-China competition is essentially about who will control the global information technology infrastructure and standards.

Frank Rose

senior fellow for security and strategy at the Brookings Institution

The China Standards 2035 blueprint is essentially the technical specifications and rules that define how many of the technologies that are in use everyday, like mobile networks, operate. Being able to influence what those look like could have wide-ranging implications for power Beijing wields in various areas of technology globally.

"U.S.-China competition is essentially about who will control the global information technology infrastructure and standards," said Frank Rose, senior fellow for security and strategy in the Foreign Policy program at The Brookings Institution, at a webinar earlier this month.

A recent report by Citi that studied AI competitiveness of 48 economies found that the U.S. still leads significantly. The other 47 economies included in the index would face "severe difficulties in catching up to the U.S.'s AI industry in 2020-30," the report said.

This was attributed to the U.S.'s strength, particularly in AI patents, investment and academic research. Citi said the ranking was not a surprise, given that major software companies are headquartered in the U.S.

The ranking was calculated by weighing five factors, namely: academic research, patents, investment, labor and hardware in the field of artificial intelligence.

However, the report also found that only China, ranked second behind the U.S. in the index, would be likely to "cultivate an independent strong ecosystem for the AI industry due to both economic and geopolitical reasons."

China would still need to catch up in two areas, namely jet engines and semiconductor, according to Michael Brown, director of the defense innovation unit at theU.S. Department of Defense.

"So they're (China) not quite there yet, but I think we can't rest on our laurels," he saidat theBrookings Institutionwebinar. "I think they very much can compete, and that's what makes me very concerned, if we don't wake up and see what we need to do to compete."

Even though many countries have invested efforts to boost their domestic biotech sector, China is the "only one whose scale could potentially ... pose a threat to American pre-eminence" in biotech, said Scott Moore, director of the Penn Global China Program at the University of Pennsylvania, during the same webinar.

China's policy target is for biotech to account for roughly 4% of Chinese GDP by 2020, and in comparison, biotech makes up around 2% of the U.S.'s GDP, Moore said.

Experts have pointed out that the U.S. could tap on alliances with other nations and re-orientate domestic policy to increase competitiveness.

"The U.S. and its allies comprise almost two-thirds of global R&D and there's extraordinary ways we can try to leverage that pool of research and development and coordinate on shared priorities," said Andrew Imbrie, senior fellow with the Center for Security and Emerging Technology at Georgetown University during the webinar.

Investing in research undertaken by both the government and academia is a "proven strategy" from the Cold War era that can be used again in this current situation, Brown said. However, "the more important and more difficult strategy" would involve the "need to reform our business thinking, and our capital markets, to move away from short-term thinking, to be more long-term oriented," he argued.

He pointed to the short-term thinking that is ingrained in the business community in the U.S. a result, he said, of measures including a focus on quarterly earnings, increasing short-term stock price, and shorter periods for holding stock.

In contrast, China takes a very long-term view, and sees technology and innovation as key to developing national capability as part of its overall national strategy, he said.

Short-term thinking isnot the right approach if the U.S. is preparing for a "superpower marathon" with China, Brown said."We have to reform this or we're not going to be successful in competing with China."

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'Superpower marathon': U.S. may lead China in tech right now but Beijing has the strength to catch up - CNBC

In just a few months, it seems we have entered a new world altogether. If we talk about the period before the SARS-CoV-2 outbreak, Artificial Intelligence was already booming and emerging as the next revolutionary thing that was all set to transform our world in a few years. However, during this pandemic period, its development has gained further momentum.

Humans are facing challenges at multiple levels, and while maintaining the social distancing and other new norms, people need to keep functioning along with learning to live with the coronavirus. The unique challenges demand new innovative solutions.

However, due to the relentless spread of the virus and increasing confirmed and probable cases, there was an urgent need to speed up the diagnosis process while maintaining similar accuracy. The newly developed AI systems could analyse a single patient's CT images in just 20 seconds with an accuracy of more than 90 per cent.

GOOD READ: Will Artificial Intelligence Barge Higher in Post-Pandemic Era?

Artificial Intelligence helped China in diagnosing COVID-19 cases

Image source: Pixabay

At the initial stages of COVID-19 outbreak, China government took the initiative to encourage developing AI tools for healthcare solutions, soon healthcare companies and tech giants in China began developing AI systems to speed up CT scan diagnosis of COVID-19 patients. Two firms in China which developed such systems are:

After these successful AI systems, multiple hospitals in China began to deploy AI CT scan diagnostic systems. Hospitals in Zhengzhou, Henan Province treating COVID-19 patients also deployed an AI-based diagnosing system. Alibaba Cloud and Alibaba DAMO Academy developed this system that can analyse CT images within 20 seconds with an accuracy of 96 per cent.

The radiology department of Zhongnan Hospital used AI software for detecting the visual signs of pneumonia associated with SARS-COV-2 infection by diagnosing lung CT scan images. Professor and chair of radiology Zhongnan Hospital, Mr Haibo Xu confirmed that the AI software proved to be extremely helpful for the staff in screening and prioritising patients who could be infected with COVID-19. Mr Haibo Xu added that only detecting pneumonia on CT scan does not confirm the person has the disease. Still, it helps the overworked staff to speed up the process of diagnosis, isolating and beginning the treatment.

RELATED: 3 Stocks Trying to Battle Novel Coronavirus Using AI-Based Technology

Other countries adopt AI technology for COVID-19 diagnosis

When the spread of the virus came out of China's borders and began to spread in other countries, they also started boosting their diagnostic capabilities with AI. In Australia, a startup that earlier provided AI diagnostic tool for improving the accuracy of breast cancer detection, modified its tool to detect the COVID-19 viral infection using lung CT scans from China and Italy.

In Russia also, Artificial Intelligence is learning to diagnose COVID-19 from the largest CT scan database that includes previous data of confirmed cases, and the new dataset, including 1,000 anonymised sets of chest CT scans.

It also includes earlier data of CT scans of patients with laboratory-confirmed infection. This earlier database was created at Diagnostics and Telemedicine Centre by scientists. The objective here is to inform AI to identify COVID-19.

The CT scan database shows the pathological abnormalities of corona infection in the lung tissue based on the chest computed tomography. The database is created according to classification and is intended to develop an AI algorithm.

Austin Health boosts COVID-Care using AI

In the Australian health care system, an AI algorithm called COVID-Care in smartphone assists recovering patients in monitoring their case each day. The patient needs to hold the smartphone up and count slowly to 30, and he/she gets diagnosed for the deadly virus and advised accordingly.

If the patient's condition aggravates, the doctor automatically gets alerted and follows up with the patient through telehealth consultation to provide advice and discuss available options. The doctor tells the patient if the patient needs to reach the hospital or is safe to stay home and do the daily assessment using COVID-Care.

AI's role in economic recovery

The economic downturn is a big challenge during COVID-19 pandemic. Once, the companies will come out of its economic hibernation; they will need to cope up fast to return to regular operations and service level. However, due to the disruptions in their workforce, this could be challenging. AI can be useful in identifying production problems, workforce behaviour, or customer service. Such disruptive times also give rise to opportunities for significant innovations. Even to survive in the changing market, there is no other way but to adopt the new technologies that can offer solutions during such turbulent times. In today's time, the companies that are implementing AI solutions to understand the changing patterns of market behaviour or their own customers base will move ahead of their competition and will provide better solutions to drive growth.

Furthermore, due to the lockdown restrictions, businesses are relying more on online channels, which also puts businesses at high risk of cybercriminal activities. AI can play a vital role in preventing such actions.

AI is based on data, so if data remains confined within individual organisations, then the technology would be of little help at each phase of crisis management. To deal with such drastic disruptions it would be unaffordable if significant data remains well-guarded and inaccessible. Thus, there must be a way out to enable acceptable data sharing to better handle any such crisis in future using technologies such as AI.

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How is Artificial Intelligence helping in dealing with the COVID-19 crisis? - Kalkine Media

Dublin, May 15, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence Markets in IVD" report has been added to ResearchAndMarkets.com's offering.

This report examines selected AI-based initiatives, collaborations, and tests in various in vitro diagnostic (IVD) market segments.

Artificial Intelligence Markets in IVD contains the following important data points:

The past few years have seen extraordinary advances in artificial intelligence (AI) in clinical medicine. More products have been cleared for clinical use, more new research-use-only applications have come to market and many more are in development.

In recent years, diagnostics companies - in collaboration with AI companies - have begun implementing increasingly sophisticated machine learning techniques to improve the power of data analysis for patient care. The goal is to use developed algorithms to standardize and aid interpretation of test data by any medical professional irrespective of expertise. This way AI technology can assist pathologists, laboratorians, and clinicians in complex decision-making.

Digital pathology products and diabetes management devices were the first to come to market with data interpretation applications. The last few years have seen the use of AI interpretation apps extended to a broader range of products including microbiology, disease genetics, and cancer precision medicine.

This report will review some of the AI-linked tests and test services that have come to market and others that are in development in some of the following market segments:

Applications of AI are evolving that predict outcomes such as diagnosis, death, or hospital readmission; that improve upon standard risk assessment tools; that elucidate factors that contribute to disease progression; or that advance personalized medicine by predicting a patient's response to treatment. AI tools are in use and in development to review data and to uncover patterns in the data that can be used to improve analyses and uncover inefficiencies. Many enterprises are joining this effort.

The following are among the companies and institutions whose innovations are featured in Artificial Intelligence Markets in IVD:

Key Topics Covered

Chapter 1: Executive Summary

Chapter 2: Artificial Intelligence In Diagnostics Markets

Chapter 3: Market Analysis: Artificial Intelligence in Diagnostics

For more information about this report visit https://www.researchandmarkets.com/r/vw8l7u

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

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Artificial Intelligence Markets in IVD, 2019-2024: Breakdown by Application and Component - GlobeNewswire

With the advancements in deep learning, the recent years have seen a humongous growth of artificial intelligence (AI) applications and services, traversing from personal assistant to recommendation systems to video/audio surveillance. All the more as of late, with the expansion of mobile computing and Internet of Things (IoT), billions of mobile and IoT gadgets are connected with the Internet, creating zillions of bytes of information at the network edge.

Driven by this pattern, there is a pressing need to push the AI frontiers to the network edge in order to completely release the potential of the edge big data. To satisfy this need, edge computing, an emerging paradigm that pushes computing undertakings and services from the network core to the network edge, has been generally perceived as a promising arrangement. The resulting new interdiscipline, edge AI or edge intelligence (EI), is starting to get an enormous amount of interest.

In any case, research on EI is still in its earliest stages, and a devoted scene for trading the ongoing advances of EI is exceptionally wanted by both the computer system and AI people group. The dissemination of EI doesnt mean, clearly, that there wont be a future for a centralized CI (Cloud Intelligence). The orchestrated utilization of Edge and Cloud virtual assets, truth be told, is required to make a continuum of intelligent capacities and functions over all the Cloudifed foundations. This is one of the significant challenges for a fruitful deployment of a successful and future-proof 5G.

Given the expanding markets and expanding service and application demands put on computational data and power, there are a few factors and advantages driving the development of edge computing. In view of the moving needs of dependable, adaptable and contextual data, a lot of the data is moving locally to on-device processing, bringing about improved performance and response time (in under a couple of milliseconds), lower latency, higher power effectiveness, improved security since information is held on the device and cost savings as data-center transports are minimized.

Probably the greatest advantage of edge computing is the capacity to make sure about real-time results for time-sensitive needs. Much of the time, sensor information can be gathered, analyzed, and communicated immediately, without sending the information to a time-sensitive cloud center. Scalability across different edge devices to help speed local decision-making is fundamental. The ability to give immediate and dependable information builds certainty, increases customer engagement, and, in many cases, saves lives. Simply think about all of the businesses, home security, aviation, car, smart cities, health care in which the immediate understanding of diagnostics and equipment performance is critical.

Indeed, recent advances in AI may have an extensive effect in various subfields of ongoing networking. For example, traffic prediction and characterization are two of the most contemplated uses of AI in the networking field. DL is likewise offering promising solutions for proficient resource management and network adoption therefore improving, even today, network system performance (e.g., traffic scheduling, routing and TCP congestion control). Another region where EI could bring performance advantages is a productive resource management and network adaption. Example issues to address traffic scheduling, routing, and TCP congestion control.

Then again, today it is somewhat challenging to structure a real-time framework with overwhelming computation loads and big data. This is where EC enters the scene. An orchestrated execution of AI methods in the computing assets in the cloud as well as at the edge, where most information is produced, will help towards this path. In addition, gathering and filtering a lot of information that contain both network profiles and performance measurements is still extremely crucial and that question turns out to be much progressively costly while considering the need of data labelling. Indeed, even these bottlenecks could be confronted by empowering EI ecosystems equipped for drawing in win-win collaborations between Network/Service Providers, OTTs, Technology Providers, Integrators and Users.

A further dimension is that a network embedded pervasive intelligence (Cloud Computing integrated with Edge Intelligence in the network nodes and smarter-and-smarter terminals) could likewise prepare to utilize the accomplishments of the developing distributed ledger technologies and platforms.

Edge computing gives an option in contrast to the long-distance transfer of data between connected devices and remote cloud servers. With a database management system on the edge devices, organizations can accomplish prompt knowledge and control and DBMS performance wipes out the reliance on latency, data rate, and bandwidth. It also lessens threats through a comprehensive security approach. Edge computing gives an environment to deal with the whole cybersecurity endeavors of the intelligent edge and the wise cloud. Binding together management systems can give intelligent threat protection.

It maintains compliance regulations entities like the General Data Protection Regulation (GDPR) that oversee the utilization of private information. Companies that dont comply risk through a significant expense. Edge computing offers various controls that can assist companies with ensuring private data and accomplish GDPR compliance.

Innovative organizations, for example, Amazon, Google, Apple, BMW, Volkswagen, Tesla, Airbus, Fraunhofer, Vodafone, Deutsche Telekom, Ericsson, and Harting are presently embracing and supporting their wagers for AI at the edge. Some of these organizations are shaping trade associations, for example, the European Edge Computing Consortium (EECC), to help educate and persuade small, medium-sized, and large enterprises to drive the adoption of edge computing within manufacturing and other industrial markets.

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The Future of Artificial Intelligence: Edge Intelligence - Analytics Insight

While weve long been enjoying some weird and wonderful mash-ups courtesy of the internets most hilarious and creative YouTubers, clearly its too much effort to be letting humans do all the work these days. As such, satirist Funk Turkey has handed the task of creating new material over to artificial intelligence, using robots to make a pretty ace AC/DCsong.

The track in question, Great Balls, came about use lyrics.rip to generate the words, before Funk channeled his best Brian Johnson to sing this hilarious mish-mash of lyrics (Wasnt the dog a touch too young to thrill? sorry, what?), and then backed it all with suitably AC/DC-esqueinstrumentation.

Read this next: Classic album covers redesigned for socialdistancing

Of course, theres hopefully real AC/DC material on the way at some point soon, with Twisted Sister vocalist Dee Snider revealing in December 2019 that all four surviving members have reunited for a new record, and, Its as close as you can get to the originalband.

Until then, though, heres Great Balls to tide usover:

In fairness, lyrics.rip is actually a pretty great little tool. We tried the same thing for Green Day to see what fine words would come out now, to get Billie Joe Armstrong to performthem:

An ambulance thats turning on the way across towncause you feeling sorry for that your whining eyesWhen September endsHere comes the waitingJust roamin for yourselfAre we are the silence with the brick of my way to search the story of my memory rests,but never forgets what I bleeding from the brick of my heads above the starsAre the waitingMy heads above the brick of self-controlTo live?My heads above the innocent can never lastTo searchthe

Okaythen.

Read this next: An exhaustive look at the phenomenon of celebrity cameos in musicvideos

Posted on May 15th 2020, 1:29pm

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This Man Created A Perfect AC/DC Song By Using Artificial Intelligence - Kerrang!

A doctor collects a throat swab specimen for the test of the novel coronavirus that causes COVID-19, at Kurla in Mumbai.

As the novel coronavirus pandemic COVID-19 continues to spread across the globe, researchers are racing against time to find possible preventive measures, tests and cures to arrest the spread. While the pandemic enters the stage of community spread in many parts of the world, countries are running short of essential medical kits to test sufficient numbers of people.

Testing is the need of the hour, and to catalyse the pace of testing, scientists have now developed an artificial intelligence-based diagnostic tool. The incredible new tool can help predict if an individual is likely to have COVID-19 disease, based on the symptoms they display. The discovery was recently published in the journal Nature Medicine.

Researchers developed the artificial intelligence-based model using data from an app called COVID Symptom Study. So far, the app is said to be downloaded by about 33 lakh people globally. The users report their health status daily on the apps, and according to the paper, the app collects data from both asymptomatic and symptomatic individuals. Besides, it tracks in real-time the disease progression by recording self-reported health information daily.

To develop the AI-based prediction system, researchers examined the data collected from about 25 lakh people in the United Kingdom and the United States between March 24 and April 21. These users actively used the app regularly to add their health status.

Based on the user data on symptoms and health status of users, the AI-based models predict who might have COVID-19. The model also uses the actual test results of the people who have been tested positive. The tool also looked into information such as test outcomes, demographics, and pre-existing medical conditions.

The research team analysed several symptoms of COVID-19, which are most likely to give positive results. These key symptoms include cold, flu, fever, cough, fatigue. Moreover, they also found loss of taste and smell, as a common characteristic of COVID-19 disease.

When the AI-based model was applied to over 800,000 app users who displayed exact symptomsrevealed about 17.42% of these people were likely to have coronavirus. Also, the tool has been proven beneficial in recognising patients who have developed mild symptoms. This could help stop the spread of the virus by making the people aware that they might be potential carriers.

The most valuable feature of this AI model is that it can predict the COVID-19 symptoms without patients getting the actual test. Particularly at the time of a pandemicthe app could prove to be of significant value for highly populated countries like India.

The Weather Companys primary journalistic mission is to report on breaking weather news, the environment and the importance of science to our lives. This story does not necessarily represent the position of our parent company, IBM.

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Artificial Intelligence to Detect Coronavirus Infection Among Individuals Without Actual Test - The Weather Channel

The way to set your children up for success in this day and age is to ensure they learn about artificial intelligence, according to the billionaire tech entrepreneur Mark Cuban.

"Give your kids an edge, have them sign up [and] learn the basics of Artificial Intelligence," Cuban tweeted on Monday.

Cuban, who is a star on the hit ABC show "Shark Tank" and the owner of the Dallas Mavericks NBA basketball team, was promoting a free, one-hour virtual class his foundation is teaching an introduction to artificial intelligence in collaboration with A.I. For Anyone, a nonprofit organization that aims to improve literacy of artificial understanding.

"Parents, want your kids to learn about artificial intelligence while you're stuck in quarantine," Cuban says on his LinkedIn account.

In the hour-long virtual class, "you'll learn what AI is, how it works, its impact on the world, and how you can best prepare for the future of AI," Cuban says on his LinkedIn account about the class. At the end of the hour-long online class, participants will receive a list of Cuban's foundation's best recommendations for AI learning resources.

(Cuban subsequently corrected the link to register.)

The event is from 7 p.m. to 8:30 p.m. EST on Wednesday, April 15.

Cuban has repeatedly used his megaphone to promote the importance of learning and understanding artificial intelligence.

At the South by Southwest conference in Austin, Texas, in March 2019, Cuban talked about how important it is for business owners to understand AI.

"As big as PCs were an impact, as big as the internet was, AI is just going to dwarf it. And if you don't understand it, you're going to fall behind. Particularly if you run a business," Cuban told Recode's Peter Kafka.

Cuban is educating himself about the future implications of AI whenever possible, he said in Austin.

"I mean, I get it on Amazon and Microsoft and Google, and I run their tutorials. If you go in my bathroom, there's a book, 'Machine Learning for Idiots.' Whenever I get a break, I'm reading it," Cuban told Kafka.

If you don't know how to write code or create an AI powered software product, at least you need to know about AI enough to be able to ask intelligent questions, Cuban said.

"If you don't know how to use it and you don't understand it and you can't at least at have a basic understanding of the different approaches and how the algorithms work," Cuban told Kafka, "you can be blindsided in ways you couldn't even possibly imagine."

Disclosure: CNBC owns the exclusive off-network cable rights to "Shark Tank."

See also:

'Shark Tank' billionaire Mark Cuban: 'If I were going to start a business today,' here's what it would be

COVID-19 pandemic proves the need for 'social robots,' 'robot avatars' and more, say experts

Bill Gates: A.I. is like nuclear energy 'both promising and dangerous'

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Mark Cuban: Here's how to give your kids 'an edge' - CNBC

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Twenty-five years after the adoption of the Beijing Declaration and Platform for Action, significant gender bias in existing social norms remains. For example, as recently as February 2020, the Indian Supreme Court had to remind the Indian government that its arguments for denying women command positions in the Army were based on stereotypes. And gender bias is not merely a male problem: a recent UNDP report entitled Tackling Social Norms found that about 90% of people (both men and women) hold some bias against women.

Gender bias and various forms of discrimination against women and girls pervades all spheres of life. Womens equal access to science and information technology is no exception. While the challenges posed by the digital divide and under-representation of women in STEM (science, technology, engineering and mathematics) continue, artificial intelligence (AI) and automation are throwing newer challenges to achieving substantive gender equality in the era of the Fourth Industrial Revolution.

If AI and automation are not developed and applied in a gender-responsive way, they are likely to reproduce and reinforce existing gender stereotypes and discriminatory social norms. In fact, this may already be happening (un)consciously. Let us consider a few examples:

Despite the potential for such gender bias, the growing crop of AI standards do not adequately integrate a gender perspective. For example, the Montreal Declaration for the Responsible Development of Artificial Intelligence does not make an explicit reference to integrating a gender perspective, while the AI4Peoples Ethical Framework for a Good AI Society mentions diversity/gender only once. Both the OECD Council Recommendation on AI and the G20 AI Principles stress the importance of AI contributing to reducing gender inequality, but provide no details on how this could be achieved.

The Responsible Machine Learning Principles do embrace bias evaluation as one of the principles. This siloed approach of embracing gender is also adopted by companies like Google and Microsoft, whose AI Principles underscore the need to avoid creating or reinforcing unfair bias and to treat all people fairly, respectively. Companies related to AI and automation should adopt a gender-response approach across all principles to overcome inherent gender bias. Google should, for example, embed a gender perspective in assessing which new technologies are socially beneficial or how AI systems are built and tested for safety.

What should be done to address the gender bias in AI and automation? The gender framework for the UN Guiding Principles on Business and Human Rights could provide practical guidance to states, companies and other actors. The framework involves a three-step cycle: gender-responsive assessment, gender-transformative measures and gender-transformative remedies. The assessment should be able to respond to differentiated, intersectional, and disproportionate adverse impacts on womens human rights. The consequent measures and remedies should be transformative in that they should be capable of bringing change to patriarchal norms, unequal power relations. and gender stereotyping.

States, companies and other actors can take several concrete steps. First, women should be active participantsrather than mere passive beneficiariesin creating AI and automation. Women and their experiences should be adequately integrated in all steps related to design, development and application of AI and automation. In addition to proactively hiring more women at all levels, AI and automation companies should engage gender experts and womens organisations from the outset in conducting human rights due diligence.

Second, the data that informs algorithms, AI and automation should be sex-disaggregated, otherwise the experiences of women will not inform these technological tools and in turn might continue to internalise existing gender biases against women. Moreover, even data related to women should be guarded against any inherent gender bias.

Third, states, companies and universities should plan for and invest in building capacity of women to achieve smooth transition to AI and automation. This would require vocational/technical training at both education and work levels.

Fourth, AI and automation should be designed to overcome gender discrimination and patriarchal social norms. In other words, these technologies should be employed to address challenges faced by women such as unpaid care work, gender pay gap, cyber bullying, gender-based violence and sexual harassment, trafficking, breach of sexual and reproductive rights, and under-representation in leadership positions. Similarly, the power of AI and automation should be employed to enhance womens access to finance, higher education and flexible work opportunities.

Fifth, special steps should be taken to make women aware of their human rights and the impact of AI and automation on their rights. Similar measures are needed to ensure that remedial mechanismsboth judicial and non-judicialare responsive to gender bias, discrimination, patriarchal power structures, and asymmetries of information and resources.

Sixth, states and companies should keep in mind the intersectional dimensions of gender discrimination, otherwise their responses, despite good intentions, will fall short of using AI and automation to accomplish gender equality. Low-income women, single mothers, women of colour, migrant women, women with disability, and non-heterosexual women all may be affected differently by AI and automation and would have differentiated needs or expectations.

Finally, all standards related to AI and automation should integrate a gender perspective in a holistic manner, rather than treating gender as merely a bias issue to be managed.

Technologies are rarely gender neutral in practice. If AI and automation continue to ignore womens experiences or to leave women behind, everyone will be worse off.

This piece is part of a blog series focusing on the gender dimensions of business and human rights. The blog series is in partnership with the Business & Human Rights Resource Centre, the Danish Institute for Human Rights and OpenGlobalRights. The views expressed in the series are those of the authors. For more on the latest news and resources on gender, business and human rights, visit thisportal.

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Addressing the gender bias in artificial intelligence and automation - OpenGlobalRights

As the COVID-19 coronavirus outbreak continues to spread across the globe, companies and researchers are looking to use artificial intelligence as a way of addressing the challenges of the virus. Here are just some of the projects using AI to address the coronavirus outbreak.

Using AI to find drugs that target the virus

A number of research projects are using AI to identify drugs that were developed to fight other diseases but which could now be repurposed to take on coronavirus. By studying the molecular setup of existing drugs with AI, companies want to identify which ones might disrupt the way COVID-19 works.

BenevolentAI, a London-based drug-discovery company, began turning its attentions towards the coronavirus problem in late January. The company's AI-powered knowledge graph can digest large volumes of scientific literature and biomedical research to find links between the genetic and biological properties of diseases and the composition and action of drugs.

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The company had previously been focused on chronic disease, rather than infections, but was able to retool the system to work on COVID-19 by feeding it the latest research on the virus. "Because of the amount of data that's being produced about COVID-19 and the capabilities we have in being able to machine-read large amounts of documents at scale, we were able to adapt [the knowledge graph] so to take into account the kinds of concepts that are more important in biology, as well as the latest information about COVID-19 itself," says Olly Oechsle, lead software engineer at BenevolentAI.

While a large body of biomedical research has built up around chronic diseases over decades, COVID-19 only has a few months' worth of studies attached to it. But researchers can use the information that they have to track down other viruses with similar elements, see how they function, and then work out which drugs could be used to inhibit the virus.

"The infection process of COVID-19 was identified relatively early on. It was found that the virus binds to a particular protein on the surface of cells called ACE2. And what we could with do with our knowledge graph is to look at the processes surrounding that entry of the virus and its replication, rather than anything specific in COVID-19 itself. That allows us to look back a lot more at the literature that concerns different coronaviruses, including SARS, etc. and all of the kinds of biology that goes on in that process of viruses being taken in cells," Oechsle says.

The system suggested a number of compounds that could potentially have an effect on COVID-19 including, most promisingly, a drug called Baricitinib. The drug is already licensed to treat rheumatoid arthritis. The properties of Baricitinib mean that it could potentially slow down the process of the virus being taken up into cells and reduce its ability to infect lung cells. More research and human trials will be needed to see whether the drug has the effects AI predicts.

Shedding light on the structure of COVID-19

DeepMind, the AI arm of Google's parent company Alphabet, is using data on genomes to predict organisms' protein structure, potentially shedding light on which drugs could work against COVID-19.

DeepMind has released a deep-learning library calledAlphaFold, which uses neural networks to predict how the proteins that make up an organism curve or crinkle, based on their genome. Protein structures determine the shape of receptors in an organism's cells. Once you know what shape the receptor is, it becomes possible to work out which drugs could bind to them and disrupt vital processes within the cells: in the case of COVID-19, disrupting how it binds to human cells or slowing the rate it reproduces, for example.

Aftertraining up AlphaFold on large genomic datasets, which demonstrate the links between an organism's genome and how its proteins are shaped, DeepMind set AlphaFold to work on COVID-19's genome.

"We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community's interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics," DeepMind said. Or, to put it another way, DeepMind hasn't tested out AlphaFold's predictions outside of a computer, but it's putting the results out there in case researchers can use them to develop treatments for COVID-19.

Detecting the outbreak and spread of new diseases

Artificial-intelligence systems were thought to be among the first to detect that the coronavirus outbreak, back when it was still localised to the Chinese city of Wuhan, could become a full-on global pandemic.

It's thought that AI-driven HealthMap, which is affiliated with the Boston Children's Hospital,picked up the growing clusterof unexplained pneumonia cases shortly before human researchers, although it only ranked the outbreak's seriousness as 'medium'.

"We identified the earliest signs of the outbreak by mining in Chinese language and local news media -- WeChat, Weibo -- to highlight the fact that you could use these tools to basically uncover what's happening in a population," John Brownstein, professor of Harvard Medical School and chief innovation officer at Boston Children's Hospital, told the Stanford Institute for Human-Centered Artificial Intelligence's COVID-19 and AI virtual conference.

Human epidemiologists at ProMed, an infectious-disease-reporting group, published their own alert just half an hour after HealthMap, and Brownstein also acknowledged the importance of human virologists in studying the spread of the outbreak.

"What we quickly realised was that as much it's easy to scrape the web to create a really detailed line list of cases around the world, you need an army of people, it can't just be done through machine learning and webscraping," he said. HealthMap also drew on the expertise of researchers from universities across the world, using "official and unofficial sources" to feed into theline list.

The data generated by HealthMap has been made public, to be combed through by scientists and researchers looking for links between the disease and certain populations, as well as containment measures. The data has already been combined with data on human movements, gleaned from Baidu,to see how population mobility and control measuresaffected the spread of the virus in China.

HealthMap has continued to track the spread of coronavirus throughout the outbreak, visualising itsspread across the world by time and location.

Spotting signs of a COVID-19 infection in medical images

Canadian startup DarwinAI has developed a neural network that can screen X-rays for signs of COVID-19 infection. While using swabs from patients is the default for testing for coronavirus, analysing chest X-rays could offer an alternative to hospitals that don't have enough staff or testing kits to process all their patients quickly.

DarwinAI released COVID-Net as an open-source system, and "the response has just been overwhelming", says DarwinAI CEO Sheldon Fernandez. More datasets of X-rays were contributed to train the system, which has now learnt from over 17,000 images, while researchers from Indonesia, Turkey, India and other countries are all now working on COVID-19. "Once you put it out there, you have 100 eyes on it very quickly, and they'll very quickly give you some low-hanging fruit on ways to make it better," Fernandez said.

The company is now working on turning COVID-Net from a technical implementation to a system that can be used by healthcare workers. It's also now developing a neural network for risk-stratifying patients that have contracted COVID-19 as a way of separating those with the virus who might be better suited to recovering at home in self-isolation, and those who would be better coming into hospital.

Monitoring how the virus and lockdown is affecting mental health

Johannes Eichstaedt, assistant professor in Stanford University's department of psychology, has been examining Twitter posts to estimate how COVID-19, and the changes that it's brought to the way we live our lives, is affecting our mental health.

Using AI-driven text analysis, Eichstaedt queried over two million tweets hashtagged with COVID-related terms during February and March, and combined it with other datasets on relevant factors including the number of cases, deaths, demographics and more, to illuminate the virus' effects on mental health.

The analysis showed that much of the COVID-19-related chat in urban areas was centred on adapting to living with, and preventing the spread of, the infection. Rural areas discussed adapting far less, which the psychologist attributed to the relative prevalence of the disease in urban areas compared to rural, meaning those in the country have had less exposure to the disease and its consequences.

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There are also differences in how the young and old are discussing COVID-19. "In older counties across the US, there's talk about Trump and the economic impact, whereas in young counties, it's much more problem-focused coping; the one language cluster that stand out there is that in counties that are younger, people talk about washing their hands," Eichstaedt said.

"We really need to measure the wellbeing impact of COVID-19, and we very quickly need to think about scalable mental healthcare and now is the time to mobilise resources to make that happen," Eichstaedt told the Stanford virtual conference.

Forecasting how coronavirus cases and deaths will spread across cities and why

Google-owned machine-learning community Kaggle is setting a number of COVID-19-related challenges to its members, includingforecasting the number of cases and fatalities by cityas a way of identifying exactly why some places are hit worse than others.

"The goal here isn't to build another epidemiological model there are lots of good epidemiological models out there. Actually, the reason we have launched this challenge is to encourage our community to play with the data and try and pick apart the factors that are driving difference in transmission rates across cities," Kaggle's CEO Anthony Goldbloom told the Stanford conference.

Currently, the community is working on a dataset of infections in 163 countries from two months of this year to develop models and interrogate the data for factors that predict spread.

Most of the community's models have been producing feature-importance plots to show which elements may be contributing to the differences in cases and fatalities. So far, said Goldbloom, latitude and longitude are showing up as having a bearing on COVID-19 spread. The next generation of machine-learning-driven feature-importance plots will tease out the real reasons for geographical variances.

"It's not the country that is the reason that transmission rates are different in different countries; rather, it's the policies in that country, or it's the cultural norms around hugging and kissing, or it's the temperature. We expect that as people iterate on their models, they'll bring in more granular datasets and we'll start to see these variable-importance plots becoming much more interesting and starting to pick apart the most important factors driving differences in transmission rates across different cities. This is one to watch," Goldbloom added.

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