Udacity partners with AWS to offer scholarships on machine learning for working professionals – Business Insider India

Posted on May 10, 2020 by admin

All applicants will be able to join the AWS Machine Learning Foundations Course. While applications are on currently, enrollment for the course begins on May 19.

This course will provide an understanding of software engineering and AWS machine learning concepts including production-level coding and practice object-oriented programming. They will also learn about deep learning techniques and its applications using AWS DeepComposer. Advertisement

A major reason behind the increasing uptake of such niche courses among the modern-age learners has to do with the growing relevance of technology across all spheres the world over. In its wake, many high-value job roles are coming up that require a person to possess immense technical proficiency and knowledge in order to assume them. And machine learning is one of the key components of the ongoing AI revolution driving digital transformation worldwide, said Gabriel Dalporto, CEO of Udacity.

The top 325 performers in the foundation course will be awarded with a scholarship to join Udacitys Machine Learning Engineer Nanodegree program. In this advanced course, the students will work on ML tools from AWS. This includes real-time projects that are focussed on specific machine learning skills.

The Nanodegree program scholarship will begin on August 19.

Understanding The Recognition Pattern Of AI – Forbes

Posted on May 10, 2020 by admin

Image and object recognition

Of the seven patterns of AI that represent the ways in which AI is being implemented, one of the most common is the recognition pattern. The main idea of the recognition pattern of AI is that were using machine learning and cognitive technology to help identify and categorize unstructured data into specific classifications. This unstructured data could be images, video, text, or even quantitative data. The power of this pattern is that were enabling machines to do the thing that our brains seem to do so easily: identify what were perceiving in the real world around us.

The recognition pattern is notable in that it was primarily the attempts to solve image recognition challenges that brought about heightened interest in deep learning approaches to AI, and helped to kick off this latest wave of AI investment and interest. The recognition pattern however is broader than just image recognition In fact, we can use machine learning to recognize and understand images, sound, handwriting, items, face, and gestures. The objective of this pattern is to have machines recognize and understand unstructured data. This pattern of AI is such a huge component of AI solutions because of its wide variety of applications.

The difference between structured and unstructured data is that structured data is already labelled and easy to interpret. However unstructured data is where most entities struggle. Up to 90% of an organization's data is unstructured data. It becomes necessary for businesses to be able to understand and interpret this data and that's where AI steps in. Whereas we can use existing query technology and informatics systems to gather analytic value from structured data, it is almost impossible to use those approaches with unstructured data. This is what makes machine learning such a potent tool when applied to these classes of problems.

Machine learning has a potent ability to recognize or match patterns that are seen in data. Specifically, we use supervised machine learning approaches for this pattern. With supervised learning, we use clean well-labeled training data to teach a computer to categorize inputs into a set number of identified classes. The algorithm is shown many data points, and uses that labeled data to train a neural network to classify data into those categories. The system is making neural connections between these images and it is repeatedly shown images and the goal is to eventually get the computer to recognize what is in the image based on training. Of course, these recognition systems are highly dependent on having good quality, well-labeled data that is representative of the sort of data that the resultant model will be exposed to in the real world. Garbage in is garbage out with these sorts of systems.

The many applications of the recognition pattern

The recognition pattern allows a machine learning system to be able to essentially look at unstructured data, categorize it, classify it, and make sense of what otherwise would just be a blob of untapped value. Applications of this pattern can be seen across a broad array of applications from medical imaging to autonomous vehicles, handwriting recognition to facial recognition, voice and speech recognition, or identifying even the most detailed things in videos and data of all types. Machine-learning enabled recognition has added significant power to security and surveillance systems, with the power to observe multiple simultaneous video streams in real time and recognize things such as delivery trucks or even people who are in a place they ought not be at a certain time of day.

The business applications of the recognition pattern are also plentiful. For example, in online retail and ecommerce industries, there is a need to identify and tag pictures for products that will be sold online. Previously humans would have to laboriously catalog each individual image according to all its attributes, tags, and categories. Nowadays, machine learning-based recognition systems are able to quickly identify products that are not already in the catalog and apply the full range of data and metadata necessary to sell those products online without any human interaction. This is a great place for AI to step in and be able to do the task much faster and much more efficiently than a human worker who is going to get tired out or bored. Not to mention these systems can avoid human error and allow for workers to be doing things of more value.

Not only is this recognition pattern being used with images, it's also used to identify sound in speech. There are lots of apps that exist that can tell you what song is playing or even recognize the voice of somebody speaking. Another application of this recognition pattern is recognizing animal sounds. The use of automatic sound recognition is proving to be valuable in the world of conservation and wildlife study. Using machines that can recognize different animal sounds and calls can be a great way to track populations and habits and get a better all-around understanding of different species. There could even be the potential to use this in areas such as vehicle repair where the machine can listen to different sounds being made by an engine and tell the operator of the vehicle what is wrong and what needs to be fixed and how soon.

One of the most widely adopted applications of the recognition pattern of artificial intelligence is the recognition of handwriting and text. While weve had optical character recognition (OCR) technology that can map printed characters to text for decades, traditional OCR has been limited in its ability to handle arbitrary fonts and handwriting. Machine learning-enabled handwriting and text recognition is significantly better at this job, in which it can not only recognize text in a wide range of printed or handwritten mode, but it can also recognize the type of data that is being recorded. For example, if there is text formatted into columns or a tabular format, the system can identify the columns or tables and appropriately translate to the right data format for machine consumption. Likewise, the systems can identify patterns of the data, such as Social Security numbers or credit card numbers. One of the applications of this type of technology are automatic check deposits at ATMs. Customers insert their hand written checks into the machine and it can then be used to create a deposit without having to go to a real person to deposit your checks.

The recognition pattern of AI is also applied to human gestures. This is something already heavily in use by the video game industry. Players can make certain gestures or moves that then become in-game commands to move characters or perform a task. Another major application is allowing customers to virtually try on various articles of clothing and accessories. It's even being applied in the medical field by surgeons to help them perform tasks and even to train people on how to perform certain tasks before they have to perform them on a real person. Through the use of the recognition pattern, machines can even understand sign language and translate and interpret gestures as needed without human intervention.

In the medical industry, AI is being used to recognize patterns in various radiology imaging. For example, these systems are being used to recognize fractures, blockages, aneurysms, potentially cancerous formations, and even being used to help diagnose potential cases of tuberculosis or coronavirus infections. Analyst firm Cognilytica is predicting that within just a few years, machines will perform the first analysis of most radiology images with instant identification of anomalies or patterns before they go to a human radiologist for further evaluation.

The recognition pattern is also being applied to identify counterfeit products. Machine-learning based recognition systems are looking at everything from counterfeit products such as purses or sunglasses to counterfeit drugs.

The use of this pattern of AI is impacting every industry from using images to get insurance quotes to analyzing satellite images after natural disasters to assess damage.Given the strength of machine learning in identifying patterns and applying that to recognition, it should come as little surprise that this pattern of AI will continue to see widespread adoption. In fact, in just a few years we might come to take the recognition pattern of AI for granted and not even consider it to be AI. That just goes to the potency of this pattern of AI. .

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Understanding The Recognition Pattern Of AI - Forbes

Major Companies in Machine Learning as a Service Market Struggle to Fulfil the Extraordinary Demand Intensified by COVID-81 – Jewish Life News

Posted on May 10, 2020 by admin

The latest report on the Machine Learning as a Service market provides an out an out analysis of the various factors that are projected to define the course of the Machine Learning as a Service market during the forecast period. The current trends that are expected to influence the future prospects of the Machine Learning as a Service market are analyzed in the report. Further, a quantitative and qualitative assessment of the various segments of the Machine Learning as a Service market is included in the report along with relevant tables, figures, and graphs. The report also encompasses valuable insights pertaining to the impact of the COVID-19 pandemic on the global Machine Learning as a Service market.

The report reveals that the Machine Learning as a Service market is expected to witness a CAGR growth of ~XX% over the forecast period (2019-2029) and reach a value of ~US$ XX towards the end of 2019. The regulatory framework, R&D activities, and technological advancements relevant to the Machine Learning as a Service market are enclosed in the report.

Request Sample Report @https://www.mrrse.com/sample/9077?source=atm

The market is segregated into different segments to provide a granular analysis of the Machine Learning as a Service market. The market is segmented on the basis of application, end-user, region, and more.

The market share, size, and forecasted CAGR growth of each Machine Learning as a Service market segment and sub-segment are included in the report.

competition landscape which include competition matrix, market share analysis of major players in the global machine learning as a service market based on their 2016 revenues and profiles of major players. Competition matrix benchmarks leading players on the basis of their capabilities and potential to grow. Factors including market position, offerings and R&D focus are attributed to companys capabilities. Factors including top line growth, market share, segment growth, infrastructure facilities and future outlook are attributed to companys potential to grow. This section also identifies and includes various recent developments carried out by the leading players.

Company profiling includes company overview, major business strategies adopted, SWOT analysis and market revenues for year 2014 to 2016. The key players profiled in the global machine learning as a service market include IBM Corporation, Google Inc., Amazon Web Services, Microsoft Corporation, BigMl Inc., FICO, Yottamine Analytics, Ersatz Labs Inc, Predictron Labs Ltd and H2O.ai. Other players include ForecastThis Inc., Hewlett Packard Enterprise, Datoin, Fuzzy.ai, and Sift Science Inc. among others.

The global machine learning as a service market is segmented as below:

By Deployment Type

By End-use Application

By Geography

Request For Discount On This Report @ https://www.mrrse.com/checkdiscount/9077?source=atm

Important Doubts Related to the Machine Learning as a Service Market Addressed in the Report:

Knowledgeable Insights Enclosed in the Report

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Major Companies in Machine Learning as a Service Market Struggle to Fulfil the Extraordinary Demand Intensified by COVID-81 - Jewish Life News

Four projects receive funding from University of Alabama CyberSeed program – Alabama NewsCenter

Posted on May 10, 2020 by admin

Four promising research projects received funding from the University of Alabama CyberSeed program, part of the UA Office for Research and Economic Development.

The pilot seed-funding program promotes research across disciplines on campus while ensuring a stimulating and well-managed environment for high-quality research.

The funded projects come from four major thrusts of the UA Cyber Initiative that include cybersecurity, critical infrastructure protection, applied machine learning and artificial intelligence, and cyberinfrastructure.

These projects are innovative in their approach to using cutting-edge solutions to tackle critical challenges, said Dr. Jeffrey Carver, professor of computer science and chair of the UA Cyber Initiative.

One project will study cybersecurity of drones and develop strategies to mitigate potential attacks. Led by Dr. Mithat Kisacikoglu, assistant professor of electrical and computer engineering, and Dr. Travis Atkison, assistant professor of computer science, the research will produce a plan for the secure design of the power electronics in drones with potential for other applications.

Another project will use machine learning to probe the nature of dark matter using existing data from NASA. The work should position the research team, led by Dr. Sergei Gleyzer, assistant professor of physics and astronomy, and Dr. Brendan Ames, assistant professor of mathematics, to analyze images expected later this year from the Vera Rubin Observatory, the worlds largest digital camera.

The CyberSeed program is also funding work planning to use machine learning to accelerate discovery of candidates within a new class of alloys that can be used in real-world experiments. These new alloys, called high-entropy alloys or multi-principal component alloys, are thought to enhance mechanical performance. This project involves Drs. Lin Li and Feng Yan, assistant professors of metallurgical and materials engineering, and Dr. Jiaqi Gong, who begins as associate professor of computer science this month.

A team of researchers is involved in a project to use state-of-the-art cyberinfrastructure technology and hardware to collect, visualize, analyze and disseminate hydrological information. The research aims to produce a proof-of-concept system. The team includes Dr. Sagy Cohen, associate professor of geography; Dr. Brad Peter, a postdoctoral researcher of geography; Dr. Hamid Moradkhani, director of the UA Center for Complex Hydrosystems; Dr. Zhe Jiang, assistant professor of computer science; Dr. D. Jay Cervino, executive director of the UA Office of Information Technology; and Dr. Andrew Molthan with NASA.

The CyberSeed program came from a process that began in April 2019 with the first internal UA cybersummit to meet and define future opportunities. In July, ORED led an internal search for the chair of the Cyber Initiative,announcing Carver in August. In October, Carver led the second internal cybersummit, at which it was agreed the Cyber Initiative would define major thrusts and develop the CyberSeed program.

While concentrating in these areas specifically, the Cyber Initiative will continue to interact with other researchers across campus to identify other promising cyber-related research areas to grow the portfolio, Carver said.

This story originally appeared on the University of Alabamas website.

Artificial Intelligence Market Report by End Use Industry …

Posted on May 10, 2020 by admin

Table of Contents

1. Executive Summary

2. Market Background and Classifications

2.1: Introduction, Background, and Classification

2.2: Supply Chain

2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2013 to 2024

3.1: Macroeconomic Trends and Forecast

3.2: Global Artificial Intelligence Market Trends and Forecast

3.3: Global Artificial Intelligence Market by End Use Industry

3.3.1: Media and Advertising

3.3.2: Security

3.3.3: Automotive

3.3.4: Healthcare

3.3.5: Retail

3.3.6: Fintech

3.3.7: Manufacturing

3.3.8: Others

3.4: Global Artificial Intelligence Market by Technology

3.4.1: Machine Learning

3.4.2: Natural Language Processing

3.4.3: Others

3.5: Global Artificial Intelligence Market by Product and Service

3.5.1: Hardware

3.5.1.1: Processor

3.5.1.2: Memory

3.5.1.3: Network

3.5.2: Software

3.5.3: Service

4. Market Trends and Forecast Analysis by Region

4.1: Global Artificial Intelligence Market by Region

4.2: North American Artificial Intelligence Market

4.2.1: Market by End Use: Media and Advertising, Security, Automotive, Healthcare, Retail, Fintech, Manufacturing, and Others

4.2.2: Market by Technology: Machine Learning, Natural Language Processing, and Others

4.2.3: Market by Product and Service: Hardware, Software, and Service

4.2.4: The US Artificial Intelligence Market

4.2.5: Canadian Artificial Intelligence Market

4.2.6: Mexican Artificial Intelligence Market

4.3: European Artificial Intelligence Market

4.3.1: Market by End Use: Media and Advertising, Security, Automotive, Healthcare, Retail, Fintech, Manufacturing, and Others

4.3.2: Market by Technology: Machine Learning, Natural Language Processing, and Others

4.3.3: Market by Product and Service: Hardware, Software, and Service

4.3.4: UK Artificial Intelligence Market

4.3.5: French Artificial Intelligence Market

4.3.6: German Artificial Intelligence Market

4.4: APAC Artificial Intelligence Market

4.4.1: Market by End Use: Media and Advertising, Security, Automotive, Healthcare, Retail, Fintech, Manufacturing, and Others

4.4.2: Market by Technology: Machine Learning, Natural Language Processing, and Others

4.4.3: Market by Product and Service: Hardware, Software, and Service

4.4.4: Chinese Artificial Intelligence Market

4.4.5: Japanese Artificial Intelligence Market

4.4.6: Indian Artificial Intelligence Market

4.5: ROW Artificial Intelligence Market

4.5.1: Market by End Use: Media and Advertising, Security, Automotive, Healthcare, Retail, Fintech, Manufacturing, and Others

4.5.2: Market by Technology: Machine Learning, Natural Language Processing, and Others

4.5.3: Market by Product and Service: Hardware, Software, and Service

5. Competitive Analysis

5.1: Product Portfolio Analysis

5.2: Geographical Reach

5.3: Porters Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

6.1: Growth Opportunity Analysis

6.1.1: Growth Opportunities for the Global Artificial intelligence Market by End Use Industry

6.1.2: Growth Opportunities for the Global Artificial intelligence Market by Technology

6.1.3: Growth Opportunities for the Global Artificial intelligence Market by Product and Service

6.1.4: Growth Opportunities for the Global Artificial intelligence Market by Region

6.2: Emerging Trends

6.3: Strategic Analysis

6.3.1: New Product Development

6.3.2: Capacity Expansion in the Global Artificial Intelligence Market

6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Artificial Intelligence Market

7. Company Profiles of Leading Players

7.1: Google

7.2: Siemens AG

7.3: Apple Inc.

7.4: Facebook

7.5: Samsung

7.6: Microsoft

7.7: Amazon

7.8: NVIDIA

7.9: NEC Corporation

7.10:Intel Corporation

7.11: IBM

7.12: General Electric

List of Figures

Chapter 2. Market Background and Classifications

Figure 2.1: AI Based Applications

Figure 2.2: AI Timeline

Figure 2.3: AI Assists Numerous Industries

Figure 2.4: Levels of AI

Artificial Intelligence | C-SPAN.org

Posted on May 10, 2020 by admin

June 26, 20182018-06-26T23:24:53-04:00https://images.c-span.org/Files/8ef/20180626103823012_hd.jpgTwo subcommittees of the House Science, Space and Technology Committee held a joint hearing to consider the applications and implications of artificial intelligence technology, or AI. Witnesses talked about the benefits of AI to U.S. society and beyond, and also addressed members' concerns about the potential nefarious applications of artificial intelligence.

Two subcommittees of the House Science, Space and Technology Committee held a joint hearing to consider the applications and implications of artificial read more

Two subcommittees of the House Science, Space and Technology Committee held a joint hearing to consider the applications and implications of artificial intelligence technology, or AI. Witnesses talked about the benefits of AI to U.S. society and beyond, and also addressed members' concerns about the potential nefarious applications of artificial intelligence. close

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*This transcript was compiled from uncorrected Closed Captioning.

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Artificial Intelligence | C-SPAN.org

Why The Way You Talk About Artificial Intelligence Needs To Change – Forbes

Posted on May 10, 2020 by admin

Gemma Milne - Author, Technology Writer

Gemma Milne isnt messing around. The world is in trouble and not just because of COVID-19. Hype and fraud fascinates her and Artificial Intelligence is one of those areas rife in both. Milne is on a mission to get everyone talking about it...properly.

Smoke + Mirrors is Milnes first book and focuses on the misuse of technical terminology. You might not think incorrectly using terms like AI is a big deal in the grand scheme of things but youd be wrong. Influence is everywhere and there is big money involved; ...it influences flow of funding, policy-making, voting, consumer behaviour, all sorts. Advertising agencies spend a great deal of time and money telling clients how important it is to sway people with narratives - their business makes no sense if words have no impact, right? says Milne.

While bluster around Blockchains ability to save the world didnt make it in the book, AI did as Milne believes the area has the biggest potential to be harmful if technology terminology is misused; There's such a cult of entrepreneurship around [AI] and a severe lack of reality in its general coverage. There are way too many people funding words on a slide deck, far too many people having philosophical discussions around the singularity as opposed to holding those in power right now to account, and far too many people still super influenced with the sci-fi narratives which have been in popular media over the last few decades.

Impact-wise, Milne believes it is tough to quantify and more funding is needed to wage a war on hype akin to fake news. Milne is clear in her intent; The point of the book is to arm each and every one of us with the insight, tools and understanding of how hype works, so we can better manage what information does to us and - ultimately - create better futures for us all. Smoke and Mirrors is there to help you fight a future we dont want to happen.

The result of doing nothing could be catastrophic for humanity. Milne believes that if we do nothing in 10-20 years well have a disjointed society made up of polar opposites; ...some in utopia, and many in dystopia: a society that doesn't always move towards new things based on inherent value but perceived value.

There is a lack of frankness in the space according to Milne - a driving force in her writing the book - There's still joy and excitement in realism - in fact, in my opinion, far more than in idealistic futurism. A sentiment more people should agree with after reading Smoke + Mirrors. I wanted to empower more people to feel able to critically think around complex topics and engage in the crucial debates happening in science and tech.

Milne is aScience & Technology Journalist (Forbes), a Keynote Speaker and the Co-Founder of Science: Disrupt. You can order SMOKE & MIRRORS on Amazon and follow her on Twitter@gemmamilne.

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Why The Way You Talk About Artificial Intelligence Needs To Change - Forbes

5 Reasons Why Artificial Intelligence Really Is Going To Change Our World – Forbes

Posted on May 10, 2020 by admin

Artificial intelligence (AI) refers to the ability of machines to interpret data and act intelligently, meaning they can make decisions and carry out tasks based on the data at hand rather like a human does.

5 Reasons Why Artificial Intelligence Really Is Going To Change Our World

Chances are youve read a lot about AI in recent years particularly how its going to save the world and/or end civilization as we know it. Its certainly true that AI attracts a lot of hype and, shall we say, colorful predictions. But, unlike some technology trends, much of the hype surrounding AI is entirely justified. It truly is a transformative technology one that will dramatically alter our lives in very real ways.

Not quite convinced? Here are five reasons why I believe AI is going to change the world in 2020 and beyond.

1. AI is everywhere

Ever asked Alexa for the morning weather report, or passed through a public space that uses facial recognition technology, or paid for something using your credit card, or bought a product recommended to you by Amazon, or browsed potential love matches on a dating app? Of course, you have. Most of us have done one or all of these things, probably in the last week. Probably in the last 24 hours.

And, you guessed it, all of these everyday processes are underpinned by AI and data. AI allows your credit card company to determine in the blink of an eye that your latest transaction fits your spending pattern and isnt fraudulent. Mastercard, for example, uses AI algorithms to assess the 75 billion transactions a year processed on its network. So, to put it bluntly, AI is already deeply embedded in your everyday life, and its not going anywhere.

2. AI isnt just infiltrating everyday life, its going to transform entire industries

The impact of AI is already being felt in a wide range of industries, from banking and retail to farming and manufacturing. In healthcare, AI is being used to identify (and, in some cases, even predict) disease, helping healthcare providers and their patients make better treatment and lifestyle decisions.

AI systems can even outperform human experts when it comes to identifying disease; in January 2020, clinical trials of AI software developed by Google Health confirmed that the software was better at spotting signs of breast cancer in mammograms than radiologists. The system also flagged fewer false positive results than the experts.

3. AI will make us more human, not less

As machines become more intelligent, they can carry out more and more tasks leading to rising automation across most industries. With this rise in automation comes valid concerns about the impact on human jobs. But, while theres no doubt that automation will lead to the displacement of many jobs, I believe it will also create new jobs jobs that value our uniquely human capabilities like creativity and empathy.

AI will also make our working lives better. Journalism is one industry thats undergoing an AI revolution, and there are many AI tools that help journalists identify and write stories. At Forbes, for example, an AI-driven content management system called Bertie is used to identify real-time trending topics, suggest improvements to headlines, and identify relevant images. This reduces some of the behind-the-scenes legwork for human journalists, leaving them to focus on telling the story.

4. AI is becoming more affordable for the masses

It used to be that to work with AI youd need expensive technology and a huge team of in-house data scientists. Thats no longer the case. Like many technology solutions, AI is now readily available on an as-a-service basis with a rapidly growing range of off-the-peg service solutions aimed at businesses of all sizes.

As an example, in 2019, Amazon launched Personalize, an AI-based service that helps businesses provide tailored customer recommendations and search results. Incredibly, Amazon says no AI experience is needed to train and deploy the technology.

5. AI fuels other technology trends

Finally, as if we needed any more evidence that AI really is going to change the world, lets end with this simple fact: AI is the foundation on which many other technology trends are built.

Essentially, this means that, without AI, we wouldnt have achieved the amazing recent advances seen in areas like virtual reality, chatbots, facial recognition, autonomous vehicles, and robotics (and thats just to name a few). Think of almost any recent transformative technology or scientific breakthrough, and, somewhere along the way, AI has played a role. For example, thanks to AI, researchers can now read and sequence genes quickly, and this knowledge can be used to determine which drug therapies will be more effective for individual patients.

AI is just one of 25 technology trends that I believe will transform our society. Read more about these key trends in my new book, Tech Trends in Practice: The 25 Technologies That Are Driving The 4th Industrial Revolution.

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5 Reasons Why Artificial Intelligence Really Is Going To Change Our World - Forbes

Artificial Intelligence is not the cure for the COVID-19 infodemic | TheHill – The Hill

Posted on May 10, 2020 by admin

More than 3 billion peoplearound 50 percent of the worlds populationengage with and post content online. Some of that content is misleading and potentially harmful, whether by design or as a side effect of its spread and manipulation. With the billions of daily active users on social media platforms, even if a mere 0.1 percent of total content contains mis or disinformation, there is a vast volume of content to review.

In response to this challenge, automated content review technologies have emerged as an enticing and scalable solution to help triage mis/disinformation online. Yet, while many technology companies and social media platforms have promoted artificial intelligence (AI) as an omnipotent tactic to address mis/disinformation, AI is not a panacea for information challenges.

In 2015, Microsoft co-founder Bill Gates gave a TED Talk that stressed our lack of preparation for the next pandemic. Fast forward to 2020, and conspiracy theorists have wrongfully used Gates TED talk as evidence to suggest that the novel coronavirus was his doing. The Gates conspiracy fueled misleading posts promoting alarming behavior, such as the creation of a dangerous and ineffective homemade, bleach-based Miracle Mineral Solution for preventing COVID-19. In this case, the repurposing of Gates words and actions made it harder for people to find and discern reliable guidance when they needed it.

The Gates conspiracy exemplifies what the World Health Organization describes as the online infodemic drawing an almost eerie linkage between the public health effects of both the viral dynamics of biology and those of online content. Ironically, these false narratives spread on the very devices and platforms that Gates and his technology sector colleagues created. These technology platformsmany of which are now powered by AI enable the viral spread of a range of mis and disinformation.

Despite what some AI evangelists may claim, AI is not equipped to independently interpret content and judge mis/disinformation. Identifying the many flavors of mis/disinformation often requires nuanced, human assessment, especially when compared to other forms of problematic content.

Content judgments are inherently complex, and they are particularly difficult during COVID-19. As the health and scientific communitys understanding of the virus continues to evolve minute by minute, the boundaries of accurate and misleading information dynamically shift. What was misleading last week (wear a mask for protection) is now the most up to date, accurate information. Further complicating an AI response, much mis/disinformation often maintains a bit of truth and authenticity. A satirical video created with no intention to cause harm may convey a point through sarcasm, or it may fool viewers who do not understand the satire. When a conspiracy theorist tweets a link to the Gates TED talk with their own misleading caption, the meaning of the original, authentic TED talk video changes with that added context.

Even if our AI systems, or AI systems in combination with human review, could perfectly and accurately adjudicate content, at the end of the day, it is human beings that must make sense of that content online. While labeling content has been touted as an effective mechanism for providing adequate disclosure to audiences making sense of material on platforms, thereby mitigating the impact of mis/disinformation, very little is known about the consequences of these labels and how audiences respond to them. Even if platforms label videos or images as manipulated, users may not understand the video to be misleading. Or worse, users may judge video or images from credible sources which do not have labeling as inherently false.

Any efforts considering broad deployment of AI systems for detecting mis/disinformation must seek to understand the human factors that are deeply integral to the success of this work. During periods of global pandemics such as COVID-19, it becomes even more vital to involve diverse, global voices of real people in both the micro content review processes and macro policy and technology decisions around mis/disinformation. Mis/disinformation challenges must be addressed in collaboration with those in civil society, media entities, and alongside social and behavioral science researchers who are already considering the human dynamics affecting public discourse. Coordinating across these disparate skillsets is difficult, but it is vital to capturing the humanistic complexity of mis/disinformation challenges that often fall on technology companies.

COVID-19 obeys the laws of science and biology, but it also affects the human body and psyche in equally impactful doses. And while mis/disinformation has certain dynamics that can be embedded into artificially intelligent systems, it also emerges in formats and contexts that are only interpretable by human judgment. Technology companies must not think about combatting mis/disinformation solely as an engineering challenge. It is a challenge that involves complex human dynamics and requires the involvement of sectors and organizations well beyond Seattle and Silicon Valley.

Claire Leibowicz is program lead on Partnership on AI.

Excerpt from:
Artificial Intelligence is not the cure for the COVID-19 infodemic | TheHill - The Hill

Is AI a More Sustainable Option Than Human Intelligence In Delivering Faster And Effective Justice In India? – India Legal

Posted on May 10, 2020 by admin

Artificial intelligenceis believed to have the ability to function and perform like that of human intelligence and possess the capability of reasoning, arguing, perceiving and acting rationally. We can say to an extent it can imitate all human functioning but this belief in itself is paradoxical.

As we talk about it on the global platform itself, the views are bifurcated some believe it as next disruptive technology which would lead to development and growth whereas some hold the view that it may lead to job losses and increase unemployment.

Researches have been made on AI towards developing such machines which can imitate human cognitive and logical skills.

Many countries have already adopted AI in judicial litigations,according to CEPEJ and the court administration of Latvia held a conference on Artificial Intelligence at the service of the judiciary, on 27th Sep 2018, it formed a platform which collaborated representatives of the academic world, professional justices, from different European countries to discuss the relevance of AI in the judicial arena, to ensure delivery of improved quality of justices, while maintaining the key fundamental principles and further highlighted the directives on which application of AI will be based upon in judicial system.

Is AI more effective? can it replace lawyers and judges?

AI is something not new as such it has already being used by the judicial system all around the world like in U.S.A to lessen the burden from the judicial system, predictive algorithms are currently being applied. various technologies such as historical crime statistics and facial recognition are also used. It is expected that this kind of automation will bring greater effectiveness in their judicial structure.

In China city of Beijing is claiming to have introduced AI-powered judge which is proclaimed to be the first of a kind and is expected to bring forth a new evolution in the era of the judiciary, it has introduced an internet-based litigation services centre which features AI judges.

Can we trust the decisions made by the AI?

The answer is still contradictory, though AI can make a different kind of legal decisions, this need not mean to be completely authentic.

The AI which is based upon using predictive algorithms by making the use of machine language, tend to rely upon the already existing uploaded data or earlier historical information. Its accuracy depends upon the quality and type of data fed in it.

As it was deduced from the research of 2008 that AI has smarter and greater potential to work efficiently being much faster and better at identifying legal issues than human intelligence could ever do. It enables the mechanization of decision making without human interferences, this goes in favour of the argument that AI can be used in the legal sector or at least it can help in giving a conclusion in legal precedences.

Can AI take the place of judges?

As it is a fact that AI has been used all over the world legislative system. They are capable of fulfilling partial work orsharing of burdens of judges and lawyersbut we cant ultimately depend on them.

Though some argue that AI can be seen as anunbiased mechanismin solving some legal cases where judgments will solely depend upon the facts, existing legal precedents which is earlier expected from human judges but as it is believed that humans judgement power could be affected by prejudice, unconsciousness and biased despite the best of their efforts put in.

Though certain things remain in the principality of philosophical debate which is at par from the knowledge of computers.

While on the other hand, some affirm that there are certain prominent roles of the judges in deciding on convicts sentence/ punishment. This could range from minor to major decisions like that of imposing long term imprisonment or the death penalty.

Those decisions embark upon certain guidelines of conviction which take into consideration the propensity of crime, its influence upon the victims, history of conviction, in this AI has already rendered its diligent role in catering fair judgements.

Judges have the power to ignore the suggestions made by AI, but that will become insignificant if human judges are completely wiped off from the legal system.

Unbiased essence of AI judges: is it feasible?

The artificial intelligence of decisions making which works on algorithms and codes are expected to be unbiased, for this aspect it sets AI as the perfect example for displaying effective legal decisions, but that sounds quite awkward because nothing can be so perfect in its true sense. So that goes with AI as well, which has to be coded by humans only, thus human intelligence is needed to manifest the super artificial intelligence into the legal framework. This can, in turn, create a condition of overlaps or unintended bias.

AI may inherent the human trait of biases of making an error then how these issues will ever solve?

The only way is not to be over-dependent on AI, they should not be treated as a final result in itself.

Can human judge supersede the decision made by AI?

So, the final answer to such questions remains uncertain as to how the government or judiciary will adopt which of these technologies.

Some benefits attached with that are trials may get faster and better with an improvised mechanism of data analysis, the overall performance and effectiveness of the legal system will improve, like that in advisory roles or gathering pieces of evidence, quite prompt decisions can be expected.Here the ethics accompanied with fairness, accountability is vital aspects of the wide acceptance of AI, which could be enabled by setting up of centre for studies on technological sustainability(CSTS) and decision-making capabilities.

But the legal fraternity cant completely rely upon AI for the final judgement and sentencing decisions. Finally, the law and the legal system needs the guidance of human intelligence. As with every passing time and advancement in society, the legal system has improved and upgraded to meet the latest need of human civilization.

Link:
Is AI a More Sustainable Option Than Human Intelligence In Delivering Faster And Effective Justice In India? - India Legal