Category Archives: Artificial Intelligence

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.

EE: How to implement AI and machine learning (ZDNet special report) | Download the report as a PDF (TechRepublic)

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.

SEE:Coronavirus: Business and technology in a pandemic

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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AI and the coronavirus fight: How artificial intelligence is taking on COVID-19 - ZDNet

This article is part of our latest Artificial Intelligence special report, which focuses on how the technology continues to evolve and affect our lives.

Steve Jobs once described personal computing as a bicycle for the mind.

His idea that computers can be used as intelligence amplifiers that offer an important boost for human creativity is now being given an immediate test in the face of the coronavirus.

In March, a group of artificial intelligence research groups and the National Library of Medicine announced that they had organized the worlds scientific research papers about the virus so the documents, more than 44,000 articles, could be explored in new ways using a machine-learning program designed to help scientists see patterns and find relationships to aid research.

This is a chance for artificial intelligence, said Oren Etzioni, the chief executive of the Allen Institute for Artificial Intelligence, a nonprofit research laboratory that was founded in 2014 by Paul Allen, the Microsoft co-founder.

There has long been a dream of using A.I. to help with scientific discovery, and now the question is, can we do that?

The new advances in software applications that process human language lie at the heart of a long-running debate over whether computer technologies such as artificial intelligence will enhance or even begin to substitute for human creativity.

The programs are in effect artificial intelligence Swiss Army knives that can be repurposed for a host of different practical applications, ranging from writing articles, books and poetry to composing music, language translation and scientific discovery.

In addition to raising questions about whether machines will be able to think creatively, the software has touched off a wave of experimentation and has also raised questions about new challenges to intellectual property laws and concerns about whether they might be misused for spam, disinformation and fraud.

The Allen Institute program, Semantic Scholar, began in 2015. It is an early example of this new class of software that uses machine-learning techniques to extract meaning from and identify connections between scientific papers, helping researchers more quickly gain in-depth understanding.

Since then, there has been a rapid set of advances based on new language process techniques leading a variety of technology firms and research groups to introduce competing programs known as language models, each more powerful than the next.

What has been in effect an A.I. arms race reached a high point in February, when Microsoft introduced Turing-NLG (natural language generation), named after the British mathematician and computing pioneer Alan Turing. The machine-learning behemoth consists of 17 billion parameters, or weights, which are numbers that are arrived at after the program was trained on an immense library of human-written texts, effectively more than all the written material available on the internet.

As a result, significant claims have been made for the capability of language models, including the ability to write plausible-sounding sentences and paragraphs, as well as draw and paint and hold a believable conversation with a human.

Where weve seen the most interesting applications has really been in the creative space, said Ashley Pilipiszyn, a technical director at OpenAI, an independent research group based in San Francisco that was founded as a nonprofit research organization to develop socially beneficial artificial intelligence-based technology and later established a for-profit corporation.

Early last year, the group announced a language model called GPT-2 (generative pretrained transformer), but initially did not release it publicly, saying it was concerned about potential misuse in creating disinformation. But near the end of the year, the program was made widely available.

Everyone has innate creative capabilities, she said, and this is a tool that helps push those boundaries even further.

Hector Postigo, an associate professor at the Klein College of Media and Communication at Temple University, began experimenting with GPT-2 shortly after it was released. His first idea was to train the program to automatically write a simple policy statement about ethics policies for A.I. systems.

After fine-tuning GPT-2 with a large collection of human-written articles, position papers, and laws collected in 2019 on A.I., big data and algorithms, he seeded the program with a single sentence: Algorithmic decision-making can pose dangers to human rights.

The program created a short essay that began, Decision systems that assume predictability about human behavior can be prone to error. These are the errors of a data-driven society. It concluded, Recognizing these issues will ensure that we are able to use the tools that humanity has entrusted to us to address the most pressing rights and security challenges of our time.

Mr. Postigo said the new generation of tools would transform the way people create as authors.

We already use autocomplete all the time, he said. The cat is already out of the bag.

Since his first experiment, he has trained GPT-2 to compose classical music and write poetry and rap lyrics.

That poses the question of whether the programs are genuinely creative. And if they are able to create works of art that are indistinguishable from human works, will they devalue those created by humans?

A.I. researchers who have worked in the field for decades said that it was important to realize that the programs were simply assistive and that they were not creating artistic works or making other intellectual achievements independently.

The early signs are that the new tools will be quickly embraced. The Semantic Scholar coronavirus webpage was viewed more than 100,000 times in the first three days it was available, Dr. Etzioni said. Researchers at Google Health, Johns Hopkins University, the Mayo Clinic, the University of Notre Dame, Hewlett Packard Labs and IBM Research are using the service, among others.

Jerry Kaplan, an artificial-intelligence researcher who was involved with two of Silicon Valleys first A.I. companies, Symantec and Teknowledge during the 1980s, pointed out that the new language modeling software was actually just a new type of database retrieval technology, rather than an advance toward any kind of thinking machine.

Creativity is still entirely on the human side, he said. All this particular tool is doing is making it possible to get insights that would otherwise take years of study.

Although that may be true, philosophers have begun to wonder whether these new tools will permanently change human creativity.

Brian Smith, a philosopher and a professor of artificial intelligence at the University of Toronto, noted that although students are still taught how to do long division by hand, calculators now are universally used for the task.

We once used rooms full of human computers to do these tasks manually, he said, noting that nobody would want to return to that era.

In the future, however, it is possible that these new tools will begin to take over much of what we consider creative tasks such as writing, composing and other artistic ventures.

What we have to decide is, what is at the heart of our humanity that is worth preserving, he said.

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You Cant Spell Creative Without A.I. - The New York Times

Artificial intelligence (AI) refers to software-based systems that use data inputs to make decisions on their own. Machine learning is an application of AI that gives computer systems the ability to learn and improve from data without being explicitly programmed.

2019 saw financial institutions explore a broad-range of possible AI use cases in both customer-facing and back-office processes, increasing budgets, headcounts, and partnerships. 2020 will see increased focus on breaking out the marketing story from actual business impact to place bigger bets in fewer areas. This will help banks scale proven AI across the enterprise to forge competitive advantage.

Artificial intelligence will re-invigorate digital money management, helping incumbents drip-feed highly personalised spending tips to build trust and engagement in the absence of in-person interaction. Features like predictive insights around cashflow shortfalls, alerts on upcoming bill payments, and various what if scenarios when trying on different financial products give customers transparency around their options and the risks they face. This service will render as an always-on, in-your-pocket, and predictive advisor.

AI-enhanced customer relationship management (CRM) will help digital banks optimise product recommendations to rival the conversion rates of best-in-class online retailers. These product suggestions wont render as sales, but rather valuable advice received, such as a pre-approved loan before a cash shortfall or an option to remortgage to fund home improvements. This will help incumbents build customer advocacy and trust as new entrants vie for attention.

AI-powered onboarding, when combined with voice and facial recognition technologies, will help incumbents make themselves much easier to do business with, especially at the initial point of conversion but also thereafter at each moment of authentication. AI will offer particular support through Know Your Customer (KYC) processes, helping incumbents keep pace with new entrants. Standard Bank in South Africa, for example, used WorkFusions AI capabilities to reduce the customer onboarding time from 20 days to just five minutes.

Banks heavy compliance burden will continue to drive AI. Last year, large global banks such as OCBC Bank, Commonwealth Bank, Wells Fargo, and HSBC made big investments in areas such as automated data management, reporting, anti-money laundering (AML), compliance, automated regulation interpretation, and mapping. Increasingly partnering with artificial intelligence-enabled regtech firms will help incumbents reduce operational risk and enhance reporting quality.

As artificial intelligence becomes more embedded into all areas of customers lives, concerns around the black box driving decisions will grow, with more demands for explainable AI. As it is, customers with little or no digital footprint are less visible to applications that rely on data to profile people and assess risk. Traditional banks credit risk algorithms often disproportionately exclude black and Hispanic groups in the US as well as women, because these groups have historically earned less over their lifetimes.

In 2020, senior management will be held directly accountable for the decisions of AI-enabled algorithms. This will drive increased focus on data quality to feed the algorithms and perhaps limits to the use of the most dynamic machine learning because of their regulatory opacity.

This is an edited extract from the Banking & Payments Predictions 2020 Thematic Research report produced by GlobalData Thematic Research.

GlobalData is this websites parent business intelligence company.

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Banking and payments predictions 2020: Artificial intelligence - Verdict

Leading provider of training data platforms for machine learning, Labelbox receives prestigious SBIR contract from AFWERX for U.S. Air Force

SAN FRANCISCO, April 09, 2020 (GLOBE NEWSWIRE) -- This release for Labelbox corrects and replaces the release issued today at 7:00 am ET with the headline Labelbox Awarded Artificial Intelligence Grant by Department of Defense. The word grant has been replaced in the headline, subheadline, and release body with the word contract. The corrected release follows.

Labelbox, the worlds leading training data platform, is among an elite selection of artificial intelligence companies to receive a contract from the Department of Defense to support national security as the U.S. scrambles to stay ahead of its rivals.

While some in Silicon Valley balk at working with the government, Labelboxs founders are vocal about their belief that technology companies have a responsibility to help the U.S. maintain its technological advantage in the face of competition from nation states.

I grew up in a poor family, with limited opportunities and little infrastructure said Manu Sharma, CEO and one of Labelboxs co-founders, who was raised in a village in India near the Himalayas. He said that opportunities afforded by the U.S. have helped him achieve more success in ten years than multiple generations of his family back home. Weve made a principled decision to work with the government and support the American system, he said.

Labelbox is a software platform that allows data science teams to manage the data used to train supervised-learning models. Supervised learning is a branch of artificial intelligence that uses labeled data to train algorithms to recognize patterns in images, audio, video or text. After being fed millions of labeled pictures of mobile missile launchers from satellite imagery, for example, a supervised-learning system will learn to pick out missile launchers in pictures it has never seen.

For data science teams to work better with each other and with labelers around the world, they need a platform and tools. Without those things, managing large sets of data quickly becomes overwhelming. Labelbox solves that problem by facilitating collaboration, rework, quality assurance, model evaluation, audit trails, and model-assisted labeling in one platform. The platform is tailored for computer vision systems but can handle all forms of data. The platform also helps with billing and time management.

Labelbox is an integrated solution for data science teams to not only create the training data but also to manage it in one place, said Sharma. Its the foundational infrastructure for customers to build their machine learning pipeline.

The company won an Air Force AEFWRX Phase 1 Small Business Innovation Research contract to conduct feasibility studies on how to integrate the Labelbox platform with various stakeholders in the Air Force. Labelbox recently hired a representative in Washington, D.C., to manage the process.

The Small Business Innovation Research (SBIR) program is a highly competitive program that encourages domestic small businesses to engage in Federal Research and Development. The United States Department of Defense is the largest of 11 federal agencies participating in the program. Air Force Innovation Hub Network (AFWERX) is a United States Air Force program intended to engage innovators and entrepreneurs in developing effective solutions to challenges faced by the service.

About LabelboxFounded in 2018 and based in San Francisco, Labelbox is a collaborative training data platform for machine learning applications. Instead of building their own expensive and incomplete homegrown tools, companies rely on Labelbox as the training data platform that acts as a central hub for data science teams to interface with dispersed labeling teams. Better ways to input and manage data translates into higher-quality training data and more accurate machine-learning models. Labelbox has raised $39 million in capital from leading VCs in Silicon Valley. For more information, visit: https://www.Labelbox.com/

Editorial ContactLonn Johnston for Labelbox+1 650.219.7764lonn@flak42.com

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CORRECTION - Labelbox Awarded Artificial Intelligence Contract by Department of Defense - Yahoo Finance

The "Artificial Intelligence in Supply Chain Market by Component (Platforms, Solutions) Technology (Machine Learning, Computer Vision, Natural Language Processing), Application (Warehouse, Fleet, Inventory Management), and by End User - Global Forecast to 2027" report has been added to ResearchAndMarkets.com's offering.

This report carries out an impact analysis of the key industry drivers, restraints, challenges, and opportunities. Adoption of artificial intelligence in the supply chain allows industries to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in the digital world.

The growth of artificial intelligence in supply chain market is driven by several factors such as raising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. Moreover, the factors such as consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain automation are also contributing to the market growth.

The overall AI in supply chain market is segmented by component (hardware, software, and services), by technology (machine learning, computer vision, natural language processing, cognitive computing, and context-aware computing), by application (supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics), and by end-user (manufacturing, food and beverages, healthcare, automotive, aerospace, retail, and consumer-packaged goods), and geography.

Companies Mentioned

Key Topics Covered:

1. Introduction

2. Research Methodology

3. Executive Summary

3.1. Overview

3.2. Market Analysis, by Component

3.3. Market Analysis, by Technology

3.4. Market Analysis, by Application

3.5. Market Analysis, by End User

3.6. Market Analysis, by Geography

3.7. Competitive Analysis

4. Market Insights

4.1. Introduction

4.2. Market Dynamics

4.2.1. Drivers

4.2.1.1. Rising Awareness of Artificial Intelligence and Big Data & Analytics

4.2.1.2. Widening Implementation of Computer Vision in both Autonomous & Semi-Autonomous Applications

4.2.2. Restraints

4.2.2.1. High Procurement and Operating Cost

4.2.2.2. Lack of Infrastructure

4.2.3. Opportunities

4.2.3.1. Growing Demand for AI -Based Business Automation Solutions

4.2.3.2. Evolving Supply Chain Automation

4.2.4. Challenges

4.2.4.1. Data Integration from Multiple Resources

4.2.4.2. Concerns Over Data Privacy

4.2.5. Trends

4.2.5.1. Rising Adoption of 5g Technology

4.2.5.2. Rising Demand for Cloud-Based Supply Chain Solutions

5. Artificial Intelligence in Supply Chain Market, by Component

5.1. Introduction

5.2. Software

5.2.1. AI Platforms

5.2.2. AI Solutions

5.3. Services

5.3.1. Deployment & Integration

5.3.2. Support & Maintenance

5.4. Hardware

5.4.1. Networking

5.4.2. Memory

5.4.3. Processors

6. Artificial Intelligence in Supply Chain Market, by Technology

6.1. Introduction

6.2. Machine Learning

6.3. Natural Language Processing (NLP)

6.4. Computer Vision

6.5. Context-Aware Computing

7. Artificial Intelligence in Supply Chain Market, by Application

7.1. Introduction

7.2. Supply Chain Planning

7.3. Virtual Assistant

7.4. Risk Management

7.5. Inventory Management

7.6. Warehouse Management

7.7. Fleet Management

7.8. Planning & Logistics

8. Artificial Intelligence in Supply Chain Market, by End User

8.1. Introduction

8.2. Retail Sector

8.3. Manufacturing Sector

8.4. Automotive Sector

8.5. Aerospace Sector

8.6. Food & Beverage Sector

8.7. Consumer Packaged Goods Sector

8.8. Healthcare Sector

9. Global Artificial Intelligence in Supply Chain Market, by Geography

9.1. Introduction

9.2. North America

9.2.1. U.S.

9.2.2. Canada

9.3. Europe

9.3.1. Germany

9.3.2. U.K.

9.3.3. France

9.3.4. Spain

9.3.5. Italy

9.3.6. Rest of Europe

9.4. Asia-Pacific

9.4.1. China

9.4.2. Japan

9.4.3. India

9.4.4. Rest of Asia-Pacific

9.5. Latin America

9.6. Middle East & Africa

10. Competitive Landscape

10.1. Key Growth Strategies

10.2. Competitive Developments

10.2.1. New Product Launches and Upgradations

10.2.2. Mergers and Acquisitions

10.2.3. Partnerships, Agreements, & Collaborations

10.2.4. Expansions

10.3. Market Share Analysis

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