New MIUI 12 Super Live Wallpapers reveals remarkable computer animations of the Planet and also Mars – Market Research News

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Xiaomi has actually presented its own brand-new MIUI 12 interface, which features many brand-new attributes and also enlargements to create a great deal a lot more enjoyable individual expertise. As well as aside from the brand-new attributes that the most recent upgrade features, there likewise is actually the brand-new MIUI 12 Live wallpaper that has actually been actually presented. As well as those are actually remarkable, to point out the minimum.

The wallpapers illustrate birds-eye views of the Earth and also Mars and also have actually been actually properly called as the Earth Live wallpaper and also the Mars Live wallpaper, specifically. Unnecessary the say, the corresponding wallpapers will definitely reveal photos of the Earth and also Mars and also are actually just remarkable.

All that you need to have to perform is actually mount the 2 online wallpaper APK data on your cell phone. Afterwards, prepared it to your phones hair display and also the residence display and also voila, your phone is going to possess a perfect remodeling of varieties.

Then there likewise is actually the MIUI 12 Super wallpaper too, which, like the various other 2 wallpapers, are actually implied for make use of on Xiaomi gadgets. That mentioned, it ought to function merely as great on any sort of Android phone. One of the very most striking elements along with the MIUI 12 Super Wallpaper is actually that it happens along with some great computer animations.

As Xiaomi explained, any sort of gadget along with the MIUI 12 Super Wallpaper set up on it is actually uncovered, the Earth or even Mars will definitely switch to show a setting. Each one of this, when seen on an AMOLED shows, creates it all appear absolutely exciting. Xiaomi likewise mentioned the Live Wallpapers would certainly enrich the electric battery lifestyle of the AMOLED gadgets.

To administer the MIUI 12 Live Wallpaper on a Xiaomi gadget, install the exact same and also head to the Gallery segment. Certainly there, choose the Live Wallpaper and also use on the 3 dots. In the food selection that opens up, choose Set Video Wallpaper. For some other Android gadgets, you will definitely need to have to install and also mount each the Live Wallpaper in addition to Google Wallpaper application coming from Play Store. Next off, introduce the Google Wallpaper application and also choose any one of the online wallpapers you merely set up. Simply touch on it and also you are actually performed.

To administer MIUI 12 Super Live Wallpaper, download and also mount MIUI 12 Super Wallpaper apk. Download and also mount the Mi Wallpaper application. Next off, download and also mount the Activity launcher application coming from Play Store. Afterwards, select Super Wallpaper and also choose the one that you want to switch on.

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New MIUI 12 Super Live Wallpapers reveals remarkable computer animations of the Planet and also Mars - Market Research News

Flood Risks for northern NZ this long weekend (+4 Day Rainfall Accumulation Map) – WeatherWatch.co.nz

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Its not often we blame a high pressure system for the chance of flooding but this weekend that may be the set up.

High pressure which is currently over the South Island will expand east of New Zealand this weekend, pulling down a very sub-tropical moisture-rich airflow which will then, in turn, be slow moving due to the high Its a bit like this rain band is building up behind a slow moving truck that it cant over take says WeatherWatch.co.nz head forecaster Philip Duncan. Like slow traffic the rain bands will get longer behind this slow moving high, ensuring some regions get saturated.

Following on from a drought heavy rain can increase the risks of slips and flooding.

WeatherWatch.co.nz, using IBM Watson (the most powerful weather super computer used in New Zealand), estimates 125 to 150mm possible in the ranges of Coromandel Peninsula. It the rain bands taken any longer to move through these numbers could actually increase says Mr Duncan.

This narrow but heavy sub-tropical flow will feed into eastern Northland, northern Auckland, eastern Coromandel Peninsula, western Bay of Plenty and then spreads out east across the rest of Bay of Plenty, East Cape, Gisborne and maybe even Hawkes Bay.

Around the outer edges of this system the rain may be more patchy with long dry spells, drizzly areas and even the odd sunny spell.

Some good rain is expected in the Auckland water catchment dams.

To drill down locally use our hourly and daily rainfall totals and risks in your local WeatherWatch.co.nz forecast or at RuralWeather.co.nz.

With the exception of the extreme rainfall numbers in some places this rain event is precisely what farmers and growers in the dry north and east have been wanting for months but its worth noting not everyone will get a soaking, with the bulk of the rain hugging the eastern coastline from Whangarei to Napier, those further west have much lower totals

Rain wont penetrate south of Banks Peninsula on the eastern side of the South Island, or about Greymouth on the western side.

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Flood Risks for northern NZ this long weekend (+4 Day Rainfall Accumulation Map) - WeatherWatch.co.nz

Virtual ICM Seminar: ‘The Promises of the One Health Concept in the Age of Anthropocen’ – HPCwire

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May 27, 2020 The Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) at the University of Warsaw invites enthusiasts of HPC and all people interested in challenging topics in Computer and Computational Science to the ICM Seminar in Computer and Computational Science that will be held on May 28, 2020 (16:00 CEST). The event is free.

On May 28, 2020, Dr. Aneta Afelt from the Interdisciplinary Centre for Mathematical and Computational Modelling department at the University of Warsaw, Espace-DEV, IRD Institut de Recherche pour le Dveloppement, will present a lecture titled, The Promises of the One Health Concept in the Age of Anthropocen

The lecture will dive into the One Health concept. In May 2019 an article was published: Anthropocene now: influential panel votes to recognize Earths new epoch situating at the stratigraphy of Earths history a new geological epoch the domination of human influence on shaping the Earths environment. When humans are a central figure in an ecological niche it results in massive subordination and transformation of the environment for their needs. Unfortunately, the outcome of such actions is a robbery of natural resources. The consequences are socially unexpected a global epidemiological crisis. The current COVID-19 pandemic is an excellent example. It seems that one of the most important questions of the anthropocene era is how to maintain stable epidemiological conditions for now and in the future. The One Health concept proposes a new paradigm a deep look at the sources of humanitys well-being: humanitys relationship with the environment. Humanitys health status is interdependent with the well-being of the environment. It is clear that the socio-ecological niche disturbance results in the spread of pathogens. Can sustainable development of socio-ecological niches help? The lecture dives into the results!

To register, visithttps://supercomputingfrontiers.eu/2020/tickets/neijis7eekieshee/

ICM Seminars is an extension of the international Supercomputing Frontiers Europe conference, which took place March 23-25th in virtual space.

The digital edition of SCFE gathered of the order of 1000 participants we want to continue this formula ofOpen Sciencemeetings despite the pandemic and use this forum to present the results of the most current research in the areas of HPC, AI, quantum computing, Big Data, IoT, computer and data networks and many others, says Dr. Marek Michalewicz, chair of the Organising Committee, SCFE2020 and ICM Seminars in Computer and Computational Science.

Registrationfor all weekly events is free. The ICM Seminars began with an inaugural lecture on April 1st by Scott Aronson, David J. Bruton Centennial Professor of Computer Science at the University of Texas. Aronson led the presentation titled Quantum Computational Supremacy and Its Applications.

For more information, visithttps://supercomputingfrontiers.eu/2020/seminars/

About the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM), University of Warsaw (UW)

Established by a resolution of the Senate of the University of Warsaw dated 29 June 1993, the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM), University of Warsaw, is one of the top HPC centres in Poland. ICM is engaged in serving the needs of a large community of computational researchers in Poland through provision of HPC and grid resources, storage, networking and expertise. It has always been an active research centre with high quality research contributions in computer and computational science, numerical weather prediction, visualisation, materials engineering, digital repositories, social network analysis and other areas.

Source: ICM UW

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The Temptations Of Artificial Intelligence Technology And The Price Of Admission – Forbes

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If your work puts you in regular contact with technology vendors, you'll have heard terms such as artificial intelligence (AI), machine learning (ML), natural language processing and computer vision before. You'll have heard that AI/ML is the future, that the boundaries of these technologies are constantly being pushed and broadened, and that AI/ML will play an integral role in shaping this tech-forward era's most successful business models.

As a technology leader, I've heard all these claims and more. To say that AI/ML will play an increasingly impactful role in business is no overstatement. According to a recent Forbes article, the machine learning market is poised to more than quadruple in the coming years.

Many industry watchers agree that AI/ML solutions, when used to good effect, can equip your organization with a significant competitive advantage. And that makes it tempting to dive right in and start implementing these technologies without first gaining a comprehensive understanding of how they work. Accessibility to myriad options is not a barrier; almost every technology vendor now offers AI/ML services. If anything, we are often inundated with choices in this domain.

But how do we know we're making the right choices and using these services to good effect? This is where a genuine, comprehensive understanding of technology becomes critically important.

For many of us, the world of AI/ML is a relatively uncharted terrain. What is artificial intelligence in modern computing? What is machine learning? The answers to these fundamental questions are the keys to unlocking the true potential of AI/ML as business solutions.

Understanding AI/ML And Its Price Of Admission

Current machine learning is a statistical process that employs a model/algorithm to explain a set of data and predict future outcomes. Many of these are "big data" algorithms that analyze huge quantities of data to generate predictions that are as accurate as possible. Once we understand this, we start to see what is required to effectively use ML as a business solution.

Simply put, we need data. We need a lot of it, and we need it to be high quality. Poor data quality is the biggest impediment to successfully adopting and deploying AI/ML solutions, and insufficient quantities of data can be a major hindrance as well.

Take IBM's Watson for oncology as a cautionary tale. After being trained on a small number of synthetic cancer cases, the Watson supercomputer was discovered to generate "erroneous cancer treatment advice" which ranged from incorrect to outright unsafe.

The data management process, which covers everything from data creation or acquisition to transmission and storage, is therefore intrinsically linked to AI initiatives. When considering the cost of implementing any AI/ML solution, it's vital to also consider the cost of obtaining a robust amount of high-quality data with which to feed that solution.

Considering AI/ML Solutions In The Context Of Your Needs

Now, with a better idea of what goes into deploying AI/ML solutions, we have to consider each of our options in the context of our vision. What do we hope to achieve by implementing AI/ML strategies?

Machines don't learn in a vacuum. Any AI/ML technology we implement will function within a web of our existing applications, interfaces and platforms. So, when crafting our vision, we need to take our organization's existing technology ecosystem into consideration.

Specificity is key in this regard. In order to choose the right model/algorithm to solve our problem, we first need to clearly define the problem we need to solve. Precise goals will help us ground our vision in reality, while a more ambiguous approach may lead to equally muddled (and unsatisfactory) results.

The Importance Of Adaptable, Unbiased Models

An effective machine learning model or algorithm must, of course, continuously learn. We won't see much success with a "set it and forget it" mentality when it comes to machine learning algorithms. If our algorithms don't rapidly adapt to changing requirements, they quickly become irrelevant and unproductive.

It's just as imperative for an algorithm to be unbiased. Cathy O'Neil, the author of Weapons of Math Destruction, spoke to NPR about the dangers of placing blind faith in the objectiveness of ML algorithms when "we really have no idea what's happening to most algorithms under the hood."

Many of the models used today across the public and private sectors certainly suffer from the prejudices and misconceptions of their designers. In 2011, a Massachusetts man was informed his driver's license had been revoked because a facial-recognition algorithm mistook him for another Massachusetts driver who was involved in criminal activity. In a similar vein,Google's hate speech detector was reported to be racially biased.

The internal workings of ML algorithms are something of a black box, which makes vigilant monitoring of their predictions extremely important. To make the most of our AI/ML solutions, we have to invest the time and attention to governing them fairly and rigorously.

You might be excited, and reasonably so, about the seemingly boundless potential of AI/ML technology. Or maybe you subscribe to Stephen Hawking's view that the development of AI could be "the worst event in the history of our civilization."

In either case, theres no question that AI/ML technology is here to stay. To make the most of it and avoid common pitfalls, we must keep in mind the fundamentals of AI/ML as we implement such solutions in our organizations.

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The Temptations Of Artificial Intelligence Technology And The Price Of Admission - Forbes

Artificial Intelligence (AI) Definition – Investopedia

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What Is Artificial Intelligence (AI)?

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.

When most people hear the term artificial intelligence, the first thing they usually think of is robots. That's because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.

As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions or recognize text through optimal character recognition are no longer considered to embody artificial intelligence, since this function is now taken for granted as an inherent computer function.

AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based in mathematics, computer science, linguistics, psychology,and more.

Algorithms often play a very important part in the structure of artificial intelligence, where simple algorithms are used in simple applications, while more complex ones help frame strong artificial intelligence.

The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for dosing drugs and different treatment in patients, and for surgical procedures in the operating room.

Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.

Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account depositsall of which help a bank's fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making supply, demand, and pricing of securities easier to estimate.

Artificial intelligence can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon's Alexa and Apple's Siri. You ask the assistant a question, it answers it for you.

Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.

Since its beginning, artificial intelligence has come under scrutiny from scientists and the public alike. One common theme is the idea that machines will become so highly developed that humans will not be able to keep up and they will take off on their own, redesigning themselves at an exponential rate.

Another is that machines can hack into people's privacy and even be weaponized.Other arguments debate the ethics of artificial intelligence and whether intelligent systems such as robots should be treated with the same rights as humans.

Self-driving cars have been fairly controversial as their machines tend to be designed for the lowest possible risk and the least casualties. If presented with a scenario of colliding with one person or another at the same time, these cars would calculate the option that would cause the least amount of damage.

Another contentious issue many people have with artificial intelligence is how it may affect human employment. With many industries looking to automate certain jobs through the use of intelligent machinery, there is a concern that people would be pushed out of the workforce. Self-driving cars may remove the need for taxis and car-share programs, while manufacturers may easily replace human labor with machines, making people's skills more obsolete.

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Artificial Intelligence (AI) Definition - Investopedia

Artificial intelligence offers a chance to optimize COVID-19 treatment in international partnership – Vanderbilt University News

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A complex artificial intelligence-powered analysis is being deployed by Jonathan Irish, associate professor of cell and developmental biology and scientific director of the Cancer & Immunology Core, in the race to understand the inner-workings of COVID-19. The tool parses through vast quantities of data to identify extremely rare immune cells that specifically respond to viruses.

Irishs analysis tool has been in development over the past year to study human immune responses to rhinovirus, a cause of the common cold, in collaboration with the University of Virginia. Upon realizing that the tool could be applied to COVID-19-related research, Irish floated the idea to his colleague at Kings College London where he is a visiting associate professor and honorary senior lecturer. This was the start of an international collaboration between Vanderbilt University and researchers from Kings College London and Guys and St Thomas NHS Foundation Trust. The group will also collaborate with researchers conducting a similar ongoing study at Princess Margaret Hospital in Canada.

High dimensional (HD) cytometry, a technique that takes measurements of many features of a single blood cell simultaneously, generates so much data that it is difficult for people to parse through. We think that HD cytometry can be particularly useful in understanding COVID-19, says Irish. The quickly developing trial will begin treating 19 patients the week of May 31, 2020, and begin collecting samples. Irishs role will be to analyze and interpret the findings. In rhinovirus, Irishs tool analyzes pairs of blood cells, one infected and the other not, to compare specific changes to the blood and identify immune cells that are reacting to the virus. But these cells known as antigen-specific T cells are one in a million, literally. A sample of 10 million blood cells might just contain a couple hundred of these cells. We quickly realized that we could tailor our tool for COVID-19 research because it can pick out these rare cells without any other information, explains Irish.

The UK-based research teams trial design is cutting-edge in the sense that it is conducting research while treating patients. This allows the team to see how the trial is working and apply new information to the ongoing trial in real-time. For example, comparing data in patients who need a ventilator with those who do not will provide an unusually clear line of sight into how the immune cells work and what to do next.

The goal of the joint research is to identify which human immune cells are specific to coronavirus infections and distinguish these cells from each persons immune fingerprint. Understanding and identifying the types of immune cells that help to fight off the virus could help us optimize vaccine and treatment strategies, notes Irish.

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Artificial intelligence offers a chance to optimize COVID-19 treatment in international partnership - Vanderbilt University News

Thanks To Renewables And Machine Learning, Google Now Forecasts The Wind – Forbes

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(Photo by Vitaly NevarTASS via Getty Images)

Wind farms have traditionally made less money for the electricity they produce because they have been unable to predict how windy it will be tomorrow.

The way a lot of power markets work is you have to schedule your assets a day ahead, said Michael Terrell, the head of energy market strategy at Google. And you tend to get compensated higher when you do that than if you sell into the market real-time.

Well, how do variable assets like wind schedule a day ahead when you don't know the wind is going to blow? Terrell asked, and how can you actually reserve your place in line?

We're not getting the full benefit and the full value of that power.

Heres how: Google and the Google-owned Artificial Intelligence firm DeepMind combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central United States. Using machine learning, they have been able to better predict wind production, better predict electricity supply and demand, and as a result, reduce operating costs.

What we've been doing is working in partnership with the DeepMind team to use machine learning to take the weather data that's available publicly, actually forecast what we think the wind production will be the next day, and bid that wind into the day-ahead markets, Terrell said in a recent seminar hosted by the Stanford Precourt Institute of Energy. Stanford University posted video of the seminar last week.

The result has been a 20 percent increase in revenue for wind farms, Terrell said.

The Department of Energy listed improved wind forecasting as a first priority in its 2015 Wind Vision report, largely to improve reliability: Improve Wind Resource Characterization, the report said at the top of its list of goals. Collect data and develop models to improve wind forecasting at multiple temporal scalese.g., minutes, hours, days, months, years.

Googles goal has been more sweeping: to scrub carbon entirely from its energy portfolio, which consumes as much power as two San Franciscos.

Google achieved an initial milestone by matching its annual energy use with its annual renewable-energy procurement, Terrell said. But the company has not been carbon-free in every location at every hour, which is now its new goalwhat Terrell calls its 24x7 carbon-free goal.

We're really starting to turn our efforts in this direction, and we're finding that it's not something that's easy to do. It's arguably a moon shot, especially in places where the renewable resources of today are not as cost effective as they are in other places.

The scientists at London-based DeepMind have demonstrated that artificial intelligence can help by increasing the market viability of renewables at Google and beyond.

Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide, said DeepMind program manager Sims Witherspoon and Google software engineer Carl Elkin. In a Deepmind blog post, they outline how they boosted profits for Googles wind farms in the Southwest Power Pool, an energy market that stretches across the plains from the Canadian border to north Texas:

Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind-power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance.

The DeepMind system predicts wind-power output 36 hours in advance, allowing power producers to make ... [+] more lucrative advance bids to supply power to the grid.

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Thanks To Renewables And Machine Learning, Google Now Forecasts The Wind - Forbes

Reality Check: The Benefits of Artificial Intelligence – AiThority

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Gartner believes Artificial Intelligence (AI) security will be a top strategic technology trend in 2020, and that enterprises must gain awareness of AIs impact on the security space. However, many enterprise IT leaders still lack a comprehensive understanding of the technology and what the technology can realistically achieve today. It is important for leaders to question exasperated Marketing claims and over-hyped promises associated with AI so that there is no confusion as to the technologys defining capabilities.

IT leaders should take a step back and consider if their company and team is at a high enough level of security maturity to adopt advanced technology such as AI successfully. The organizations business goals and current focuses should align with the capabilities that AI can provide.

A study conducted by Widmeyer revealed that IT executives in the U.S. believe that AI will significantly change security over the next several years, enabling IT teams to evolve their capabilities as quickly as their adversaries.

Of course, AI can enhance cybersecurity and increase effectiveness, but it cannot solve every threat and cannot replace live security analysts yet. Today, security teams use modern Machine Learning (ML) in conjunction with automation, to minimize false positives and increase productivity.

As adoption of AI in security continues to increase, it is critical that enterprise IT leaders face the current realities and misconceptions of AI, such as:

AI is not a solution; it is an enhancement. Many IT decision leaders mistakenly consider AI a silver bullet that can solve all their current IT security challenges without fully understanding how to use the technology and what its limitations are. We have seen AI reduce the complexity of the security analysts job by enabling automation, triggering the delivery of cyber incident context, and prioritizing fixes. Yet, security vendors continue to tout further, exasperated AI-enabled capabilities of their solution without being able to point to AIs specific outcomes.

If Artificial Intelligence is identified as the key, standalone method for protecting an organization from cyberthreats, the overpromise of AI coupled with the inability to clearly identify its accomplishments, can have a very negative impact on the strength of an organizations security program and on the reputation of the security leader. In this situation, Chief Information Security Officers (CISO) will, unfortunately, realize that AI has limitations and the technology alone is unable to deliver aspired results.

This is especially concerning given that 48% of enterprises say their budgets for AI in cybersecurity will increase by 29 percent this year, according to Capgemini.

Read more:Improve Your Bottom Line With Contract Automation and AI

We have seen progress surrounding AI in the security industry, such as the enhanced use of ML technology to recognize behaviors and find security anomalies. In most cases, security technology can now correlate the irregular behavior with threat intelligence and contextual data from other systems. It can also use automated investigative actions to provide an analyst with a strong picture of something being bad or not with minimal human intervention.

A security leader should consider the types of ML models in use, the biases of those models, the capabilities possible through automation, and if their solution is intelligent enough to build integrations or collect necessary data from non-AI assets.

AI can handle a bulk of the work of a Security Analyst but not all of it. As a society, we still do not have enough trust in AI to take it to the next level which would be fully trusting AI to take corrective actions towards those anomalies it identified. Those actions still require human intervention and judgment.

Read more:The Nucleus of Statistical AI: Feature Engineering Practicalities for Machine Learning

It is important to consider that AI can make bad or wrong decisions. Given that humans themselves create and train the models that achieve AI, it can make biased decisions based on the information it receives.

Models can produce a desired outcome for an attacker, and security teams should prepare for malicious insiders to try to exploit AI biases. Such destructive intent to influence AIs bias can prove to be extremely damaging, especially in the legal sector.

By feeding AI false information, bad actors can trick AI to implicate someone of a crime more directly. As an example, just last year, a judge ordered Amazon to turn over Echo recordings in a double murder case. In instances such as these, a hacker has the potential to wrongfully influence ML models and manipulate AI to put an innocent person in prison. In making AI more human, the likelihood of mistakes will increase.

Whats more, IT decision-makers must take into consideration that attackers are utilizing AI and ML as an offensive capability. AI has become an important tool for attackers, and according to Forresters Using AI for Evil report, mainstream AI-powered hacking is just a matter of time.

AI can be leveraged for good and for evil, and it is important to understand the technologys shortcomings and adversarial potential.

Though it is critical to acknowledge AIs realistic capabilities and its current limitations, it is also important to consider how far AI can take us. Applying AI throughout the threat lifecycle will eventually automate and enhance entire categories of Security Operations Center (SOC) activity. AI has the potential to provide clear visibility into user-based threats and enable increasingly effective detection of real threats.

There are many challenges IT decision-makers face when over-estimating what Artificial Intelligence alone can realistically achieve and how it impacts their security strategies right now. Security leaders must acknowledge these challenges and truths if organizations wish to reap the benefits of AI today and for years to come.

Read more:AI in Cybersecurity: Applications in Various Fields

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Artificial intelligence is hopelessly biased – and that’s how it will stay – TechRadar India

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Much has been said about the potential of artificial intelligence (AI) to transform many aspects of business and society for the better. In the opposite corner, science fiction has the doomsday narrative covered handily.

To ensure AI products function as their developers intend - and to avoid a HAL9000 or Skynet-style scenario - the common narrative suggests that data used as part of the machine learning (ML) process must be carefully curated, to minimise the chances the product inherits harmful attributes.

According to Richard Tomsett, AI Researcher at IBM Research Europe, our AI systems are only as good as the data we put into them. As AI becomes increasingly ubiquitous in all aspects of our lives, ensuring were developing and training these systems with data that is fair, interpretable and unbiased is critical.

Left unchecked, the influence of undetected bias could also expand rapidly as appetite for AI products accelerates, especially if the means of auditing underlying data sets remain inconsistent and unregulated.

However, while the issues that could arise from biased AI decision making - such as prejudicial recruitment or unjust incarceration - are clear, the problem itself is far from black and white.

Questions surrounding AI bias are impossible to disentangle from complex and wide-ranging issues such as the right to data privacy, gender and race politics, historical tradition and human nature - all of which must be unraveled and brought into consideration.

Meanwhile, questions over who is responsible for establishing the definition of bias and who is tasked with policing that standard (and then policing the police) serve to further muddy the waters.

The scale and complexity of the problem more than justifies doubts over the viability of the quest to cleanse AI of partiality, however noble it may be.

Algorithmic bias can be described as any instance in which discriminatory decisions are reached by an AI model that aspires to impartiality. Its causes lie primarily in prejudices (however minor) found within the vast data sets used to train machine learning (ML) models, which act as the fuel for decision making.

Biases underpinning AI decision making could have real-life consequences for both businesses and individuals, ranging from the trivial to the hugely significant.

For example, a model responsible for predicting demand for a particular product, but fed data relating to only a single demographic, could plausibly generate decisions that lead to the loss of vast sums in potential revenue.

Equally, from a human perspective, a program tasked with assessing requests for parole or generating quotes for life insurance plans could cause significant damage if skewed by an inherited prejudice against a certain minority group.

According to Jack Vernon, Senior Research Analyst at IDC, the discovery of bias within an AI product can, in some circumstances, render it completely unfit for purpose.

Issues arise when algorithms derive biases that are problematic or unintentional. There are two usual sources of unwanted biases: data and the algorithm itself, he told TechRadar Pro via email.

Data issues are self-explanatory enough, in that if features of a data set used to train an algorithm have problematic underlying trends, there's a strong chance the algorithm will pick up and reinforce these trends.

Algorithms can also develop their own unwanted biases by mistake...Famously, an algorithm for identifying polar bears and brown bears had to be discarded after it was discovered the algorithm based its classification on whether there was snow on the ground or not, and didn't focus on the bear's features at all.

Vernons example illustrates the eccentric ways in which an algorithm can diverge from its intended purpose - and its this semi-autonomy that can pose a threat, if a problem goes undiagnosed.

The greatest issue with algorithmic bias is its tendency to compound already entrenched disadvantages. In other words, bias in an AI product is unlikely to result in a white-collar banker having their credit card application rejected erroneously, but may play a role in a member of another demographic (which has historically had a greater proportion of applications rejected) suffering the same indignity.

The consensus among the experts we consulted for this piece is that, in order to create the least prejudiced AI possible, a team made up of the most diverse group of individuals should take part in its creation, using data from the deepest and most varied range of sources.

The technology sector, however, has a long-standing and well-documented issue with diversity where both gender and race are concerned.

In the UK, only 22% of directors at technology firms are women - a proportion that has remained practically unchanged for the last two decades. Meanwhile, only 19% of the overall technology workforce are female, far from the 49% that would accurately represent the ratio of female to male workers in the UK.

Among big tech, meanwhile, the representation of minority groups has also seen little progress. Google and Microsoft are industry behemoths in the context of AI development, but the percentage of black and Latin American employees at both firms remains miniscule.

According to figures from 2019, only 3% of Googles 100,000+ employees were Latin American and 2% were black - both figures up by only 1% over 2014. Microsofts record is only marginally better, with 5% of its workforce made up of Latin Americans and 3% black employees in 2018.

The adoption of AI in enterprise, on the other hand, skyrocketed during a similar period according to analyst firm Gartner, increasing by 270% between 2015-2019. The clamour for AI products, then, could be said to be far greater than the commitment to ensuring their quality.

Patrick Smith, CTO at data storage firm Pure Storage, believes businesses owe it not just to those that could be affected by bias to address the diversity issue, but also to themselves.

Organisations across the board are at risk of holding themselves back from innovation if they only recruit in their own image. Building a diversified recruitment strategy, and thus a diversified employee base, is essential for AI because it allows organisations to have a greater chance of identifying blind spots that you wouldnt be able to see if you had a homogenous workforce, he said.

So diversity and the health of an organisation relates specifically to diversity within AI, as it allows them to address unconscious biases that otherwise could go unnoticed.

Further, questions over precisely how diversity is measured add another layer of complexity. Should a diverse data set afford each race and gender equal representation, or should representation of minorities in a global data set reflect the proportions of each found in the world population?

In other words, should data sets feeding globally applicable models contain information relating to an equal number of Africans, Asians, Americans and Europeans, or should they represent greater numbers of Asians than any other group?

The same question can be raised with gender, because the world contains 105 men for every 100 women at birth.

The challenge facing those whose goal it is to develop AI that is sufficiently impartial (or perhaps proportionally impartial) is the challenge facing societies across the globe. How can we ensure all parties are not only represented, but heard - and when historical precedent is working all the while to undermine the endeavor?

The importance of feeding the right data into ML systems is clear, correlating directly with AIs ability to generate useful insights. But identifying the right versus wrong data (or good versus bad) is far from simple.

As Tomsett explains, data can be biased in a variety of ways: the data collection process could result in badly sampled, unrepresentative data; labels applied to the data through past decisions or human labellers may be biased; or inherent structural biases that we do not want to propagate may be present in the data.

Many AI systems will continue to be trained using bad data, making this an ongoing problem that can result in groups being put at a systemic disadvantage, he added.

It would be logical to assume that removing data types that could possibly inform prejudices - such as age, ethnicity or sexual orientation - might go some way to solving the problem. However, auxiliary or adjacent information held within a data set can also serve to skew output.

An individuals postcode, for example, might reveal much about their characteristics or identity. This auxiliary data could be used by the AI product as a proxy for the primary data, resulting in the same level of discrimination.

Further complicating matters, there are instances in which bias in an AI product is actively desirable. For example, if using AI to recruit for a role that demands a certain level of physical strength - such as firefighter - it is sensible to discriminate in favor of male applicants, because biology dictates the average male is physically stronger than the average female. In this instance, the data set feeding the AI product is indisputably biased, but appropriately so.

This level of depth and complexity makes auditing for bias, identifying its source and grading data sets a monumentally challenging task.

To tackle the issue of bad data, researchers have toyed with the idea of bias bounties, similar in style to bug bounties used by cybersecurity vendors to weed out imperfections in their services. However, this model operates on the assumption an individual is equipped to to recognize bias against any other demographic than their own - a question worthy of a whole separate debate.

Another compromise could be found in the notion of Explainable AI (XAI), which dictates that developers of AI algorithms must be able to explain in granular detail the process that leads to any given decision generated by their AI model.

Explainable AI is fast becoming one of the most important topics in the AI space, and part of its focus is on auditing data before its used to train models, explained Vernon.

The capability of AI explainability tools can help us understand how algorithms have come to a particular decision, which should give us an indication of whether biases the algorithm is following are problematic or not.

Transparency, it seems, could be the first step on the road to addressing the issue of unwanted bias. If were unable to prevent AI from discriminating, the hope is we can at least recognise discrimination has taken place.

The perpetuation of existing algorithmic bias is another problem that bears thinking about. How many tools currently in circulation are fueled by significant but undetected bias? And how many of these programs might be used as the foundation for future projects?

When developing a piece of software, its common practice for developers to draw from a library of existing code, which saves time and allows them to embed pre-prepared functionalities into their applications.

The problem, in the context of AI bias, is that the practice could serve to extend the influence of bias, hiding away in the nooks and crannies of vast code libraries and data sets.

Hypothetically, if a particularly popular piece of open source code were to exhibit bias against a particular demographic, its possible the same discriminatory inclination could embed itself at the heart of many other products, unbeknownst to their developers.

According to Kacper Bazyliski, AI Team Leader at software development firm Neoteric, it is relatively common for code to be reused across multiple development projects, depending on their nature and scope.

If two AI projects are similar, they often share some common steps, at least in data pre- and post-processing. Then its pretty common to transplant code from one project to another to speed up the development process, he said.

Sharing highly biased open source data sets for ML training makes it possible that the bias finds its way into future products. Its a task for the AI development teams to prevent from happening.

Further, Bazyliski notes that its not uncommon for developers to have limited visibility into the kinds of data going into their products.

In some projects, developers have full visibility over the data set, but its quite often that some data has to be anonymized or some features stored in data are not described because of confidentiality, he noted.

This isnt to say code libraries are inherently bad - they are no doubt a boon for the worlds developers - but their potential to contribute to the perpetuation of bias is clear.

Against this backdrop, it would be a serious mistake to...conclude that technology itself is neutral, reads a blog post from Google-owned AI firm DeepMind.

Even when bias does not originate with software developers, it is still repackaged and amplified by the creation of new products, leading to new opportunities for harm.

Bias is an inherently loaded term, carrying with it a host of negative baggage. But it is possible bias is more fundamental to the way we operate than we might like to think - inextricable from the human character and therefore anything we produce.

According to Alexander Linder, VP Analyst at Gartner, the pursuit of impartial AI is misguided and impractical, by virtue of this very human paradox.

Bias cannot ever be totally removed. Even the attempt to remove bias creates bias of its own - its a myth to even try to achieve a bias-free world, he told TechRadar Pro.

Tomsett, meanwhile, strikes a slightly more optimistic note, but also gestures towards the futility of an aspiration to total impartiality.

Because there are different kinds of bias and it is impossible to minimize all kinds simultaneously, this will always be a trade-off. The best approach will have to be decided on a case by case basis, by carefully considering the potential harms from using the algorithm to make decisions, he explained.

Machine learning, by nature, is a form of statistical discrimination: we train machine learning models to make decisions (to discriminate between options) based on past data.

The attempt to rid decision making of bias, then, runs at odds with the very mechanism humans use to make decisions in the first place. Without a measure of bias, AI cannot be mobilised to work for us.

It would be patently absurd to suggest AI bias is not a problem worth paying attention to, given the obvious ramifications. But, on the other hand, the notion of a perfectly balanced data set, capable of rinsing all discrimination from algorithmic decision-making, seems little more than an abstract ideal.

Life, ultimately, is too messy. Perfectly egalitarian AI is unachievable, not because its a problem that requires too much effort to solve, but because the very definition of the problem is in constant flux.

The conception of bias varies in line with changes to societal, individual and cultural preference - and it is impossible to develop AI systems within a vacuum, at a remove from these complexities.

To be able to recognize biased decision making and mitigate its damaging effects is critical, but to eliminate bias is unnatural - and impossible.

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Artificial intelligence is hopelessly biased - and that's how it will stay - TechRadar India

Top Artificial Intelligence Trends that will Change the Decade – Analytics Insight

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As we began the new decade, technology is changing by leaps and bounds. The initial predictions for 2020 point to a serious integration of AI and human experience to study how Intelligent Automation technologies can be used to augment an enterprise experience.

Here are the top Artificial Intelligence (AI) trends that will change the decade:

The new decade will witness massive investments by global technology giants into AI technologies. In 2020, many factories of AI models and data will emerge helping AI technology and associated commercial solutions on a large-scale facilitating the enterprise. For instance, AI solutions in the customer service industry find its use cases in e-commerce, education, finance and related industries on a large scale.

Digital IQ will rise in this decade. Digital Intelligence is defined as the measurement of how organizations understand its business processes and the content and data within them from a variety of critical perspectives.

Digital Intelligence solutions assist enterprises by optimizing automation initiatives and complementing platforms like business process management and robotic process automation. 2020 will witness more and more enterprises adopting digital intelligence technologies into their digital transformation initiatives.

Deep learning is imperative to the development threshold of AI technology improving the quality and efficiency of AI applications. In 2020 and beyond, deep learning will be applied across multi-industries at a scale to accelerate transformation, upgrading and implement innovation.

According to the IDC (International Data Corporation) research, digital workers like software robots and cognitive bots will witness a growth of over 50% by 2022. Enterprises will welcome many digital robots willing to take up rule-based tasks in the office. Employees across geographies will collaborate with digital workers working alongside them in the future.

Individual technology systems like ERP, CRM, CMS, EHR, etc provides visibility into the processes controlled by their platform. To gain visibility, organizations will need to leverage Process Intelligence technologies which provide an accurate, comprehensive and real-time view of all processes across multiple functionalities, departments, personnel, functions across different locations.

In 2020 and beyond, AI will not only benefit the user experience but will be increasingly adopted by business users across geographies. Enterprises will leverage the internal marketplaces of robots and other easy-to-use automation tools available to across technical proficiencies. These new platforms will play a pivotal role in improving how employees get work done to improve customer experiences better than the competition.

Enabling cognitive automation will require new tools built for the task. AI-enabled Process and Content Intelligence technologies will provide digital workers with the skills and understanding necessary to deal with natural language, reasoning, and judgment, establishing context, providing data-driven insights.

The normalcy of AI in the workplace will also be the reason we see more human interaction with AI.

With the successful demonstration of quantum hegemony, quantum computing will usher in a new round of explosive growth in 2020. In terms of quantum hardware, the performance of programmable medium-sized noisy quantum devices will be further improved and have the ability of error correction. Quantum algorithms with certain practical value will be able to run on them, and the application of quantum artificial intelligence will be greatly developed.

In terms of quantum software, high-quality quantum computing platforms and software will emerge and be deeply integrated with AI and cloud computing technologies. Besides, with the emergence of the quantum computing industry chain, quantum computing will surely garner more attention in more application fields.

Organizations big and small will now invest in systems and methods to collect and record all the data they can, in a bid to improve their business process and functionalities.

The rapid growth in data, the reduced cost in storage, and the ease to access the data have shown incredible growth from the last decade. Data is driving the improvement of the customer experience, advancing analytics capabilities, allowing businesses to harness real value from intelligent automation, and enabling machine learning and AI that is driven by data.

Artificial intelligence can reshape and redefine the way we work and live. The growing trend we expect to see, and more is the integration of AI-enabled solutions in the workplace. These tools will help create better outcomes, ensuring enterprises are achieving their goals in a timely and efficient fashion setting new user experiences. When thinking about the needs of the hybrid workforce, leaders need to decide if simple task-based automation tools are the answer to their problems, or if they will require a mix of AI and other transformative technologies to achieve the next-gen intelligent and cognitive automation.

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

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Art and artifice – The Indian Express

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By: Editorial | Updated: May 30, 2020 7:46:48 am An AI developed in Vienna is now debuting in the art business, and will curate the Bucharest Biennale.

Practitioners in the arts labour under the misapprehension that the human factor of creativity would shield them from the depredations of artificial intelligence. It is assumed that like machines freed us from physical labour, machine intelligence would rid us of intellectual chores. They would put production line workers, bookkeepers, bank tellers and inventory managers out of work, but novelists and artists, and the marketing networks which have developed around their products, would be unharmed.

Not so, it appears. A computer at Stanford which has digested the complete works of Shakespeare does almost passable knockoffs. In 2018, a neural network went on a journey across America and wrote a digital equivalent of Jack Kerouacs Beat classic On the Road. The 1957 original was strange enough. But 1 the Road, written by a computer system, is stranger still, depending on literary devices that the human mind finds perplexing, like GPS data.

An AI developed in Vienna is now debuting in the art business, and will curate the Bucharest Biennale. Jarvis, named for the archetypal butler, will thematically select works from the databases of galleries and institutions, and display them in virtual reality. But an AI is only as good as the datasets it is fed. Lets suppose Jarvis is looking for portraiture, selects Vermeers haunting Girl with a Pearl Earring, erroneously supposes that earrings are essential to the form, and unearths the awful portraits of bejewelled nobility lurking in the stately homes of Europe. At that point, like a god from the machine, a human must step in.

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The Role of Artificial Intelligence in Ethical Hacking | EC-Council Official Blog – EC-Council Blog

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Artificial intelligence has influenced every aspect of our daily lives. Nowadays,thousands of tech companies have developed state-of-the-art AI-powered cybersecurity defense solutions specifically designed and programmed by ethical hackers and penetration testers. The Artificial Intelligence used in such solutions helps prevent cyberattacks from even happening by predicting the potential risks.

Every tech app or service we use contains at least some type of Artificial Intelligence or smart learning technology, at least in most cases. It is now of a high possibility of connecting almost every electrical device to the internet to create our own personalized smart environments, all thanks to the recently announced 5th generation speed networks and rapid advancement in machine learning. In order to improve the overall efficiency and performance of the tasks these IoT devices are set to, they have to communicate and exchange information with each other repeatedly.

With the use of AI, most of the tasks are automated, and these AI-powered apps are used in every sector like healthcare, education, military, etc. The data from these smart devices is collected from a set of sensors such as heat, light, weight, speed, or noise. The machine learning technology is directly connected with the security of digital devices and our data/information. There has been an extensive progression in the artificial intelligence sector. And in todays modern age, where all our devices are connected either to the internet or some other modes of networks, the risks of security issues and the need for Artificial Intelligence solutions have skyrocketed.

Different Artificial Intelligence solutions help when our devices are involved in heavy communication under the connection of networks, automatically sending our data securely to the remote cloud servers where all the data and information are gathered and analyzed by the robotic process in order to understand, visualize and extract useful information. Now the challenge arises in safely sending this confidential data to servers as there are many potential threats and risks of hackers stealing this data, which can, in turn, lead a device to be used illegitimately, leaving a privacy risk. Therefore, we must have a way of securing these devices and overcoming these risks in the form of a solution that involves high usage of AI-powered applications.

Since we all use the web regularly, the most common medium we use to access it is an internet browser. And cybercrooks, due to their malicious nature, have found several mechanisms to deceive the innocent and unsuspecting users into providing sensitive information through phishing. This method works when a cybercrook makes a fake SMS, video, phone call, or shopping site that offers goods, products, or services at very unbelievable prices. But when a user enters their personal information, like credit card or other payment information, it goes straight into the hands of web-hackers who then use this for their very own personal usage. Then the innocent buyer never receives anything that they ordered. However, cyber professionals have deemed artificial intelligence as a countermeasure for this hacking method. The AI-powered web-based filters and firewall applications are now available that upon deployment on a users device like their computer;it protects the users by now even letting the user open a website that raises flags of a little bit of insecure and suspicious. Machine learning coded into Artificial Intelligence helps learn the new patterns of scams over time, and these AI-powered firewalls learn automatically new ways to protect users against advanced cyber risks. When users install these anti-scams firewalls into their digital devices like mobile phones, then the chances of phishing related scams are extremely reduced.

Data leaks and identity theft nowadays are on the rise, as the passing time has very much revealed the shocking fact that the number of such cyber-attacks is only increasing over time. As humans, we are not perfect, and we also keep forgetting very important things in our lives. The same goes for when it comes to the protection of data/information like our passwords for our different accounts, including social media, bank accounts, and so on. Since we keep the usage of the same passwords for too long that the chances of it being break/cracked by cyber-crooks increase. Or simply sometimes we keep a device logged-in with our info, and someone else physically takes the device and can see our private information. But with the use and implementation of AI, this problem is hugely reduced as Artificial Intelligence automatically determines that a password of an account is being used for too long, and it is time to change, keeping the user reminding that changing-passcodes regularly is vital for information-safeguarding. Similarly, if an account is open and someone tries to change, edit or modify info/data the Artificial Intelligence parameters are automatically triggered that asks the users to re-verify their passcodes, hence protecting the user from ID/data theft

Lets face it, everyone uses the internet, and for using the internet we need a web-browser. Its common knowledge that there are billions of people who use the internet everyday and for hours. We use browsers on every device we own no matter its our smartphone, tab, laptops. The Internet is a crucial part of our lifestyle; there is no denying this fact. However, Artificial Intelligence only has the power to predict potential cybersecurity risks and take feasible countermeasures and to block them even before time. However, practicing privacy enhancement methods plays a vital role in developing secure habits that can save us, users from any malicious attack attempt on our IoT devices. Obtaining the EC-Council Certified Ethical Hacker (CEH) Certification would give you the critical base-knowledge of implementing Artificial Intelligence into a cybersecurity environment.

FAQs

How does Artificial Intelligence help cyber security?

Read more: https://becominghuman.ai/why-you-should-use-artificial-intelligence-in-cybersecurity-204dbe33326c

Will Artificial Intelligence take over cyber security?

Read more: https://www.circadence.com/blog/will-artificial-intelligence-replace-cyber-security-jobs/

What is the future for cyber security?

The ability to leverage machine learning and artificial intelligence is the future of cybersecurity. There is no doubt Artificial Intelligence can become the future of security. Data is exponentially increasing. Automation and machine learning have catapulted us beyond the limitations of human skill.

Read more: https://www.disruptordaily.com/future-of-cybersecurity/

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The Role of Artificial Intelligence in Ethical Hacking | EC-Council Official Blog - EC-Council Blog

Artificial intelligence software improves accuracy, doubles speed in evaluating CT scans of advanced cancer – UAB News

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An ONeal Comprehensive Cancer Center scientist presents at a major oncology meeting about a novel artificial intelligence software tool to assist evaluating tumor response in advanced cancers.

An ONeal Comprehensive Cancer Center scientist presents at a major oncology meeting about a novel artificial intelligence software tool to assist evaluating tumor response in advanced cancers.In a multi-institution study presented this week at the annual meeting of the American Society of Clinical Oncology, researchers from the ONeal Comprehensive Cancer Center at the University of Alabama at Birmingham compared the current practice of evaluating tumor response in advanced cancer with an artificial intelligence software tool designed to assist radiologists.

It turns out that human-guided AI is more accurate, more reproducible and faster, said Andrew Smith, M.D., Ph.D., associate professor, vice chair of Clinical Research and co-director of AI in the UAB Department of Radiology, and director of the Tumor Metrics Lab, a component of the ONeal Cancer Centers Human Imaging shared resource, who presented the findings today at ASCO.

The Tumor Metrics Lab conducts image interpretation for all cancer patients on clinical trials in the ONeal Cancer Center who have tumor imaging to determine their response to treatment. The lab does more than 1,000 tumor metric reads per year.

Doctors track the progress of tumors using computed tomography scans. Radiologists measure the tumors manually on digital images of those scans and usually dictate their findings into text-based reports. But the group of researchers behind the new study hypothesized that this traditional system could be improved with some assistance from artificial intelligence. They used AI Mass, a cancer-specific implementation of the medical AI software platform AI Metrics, trained with more than 15,000 expert-labeled images.

AI Mass uses AI to, one, measure tumors after a single mouse click; two, automatically label the anatomic location of tumors; and three, track tumors over time, Smith said. AI Metrics is a product of a startup company of the same name, with Smith as CEO, that was spun off from UAB in 2019 and is now raising a seed round of capital.

In the study presented at ASCO by Smith, body CT images from 120 consecutive patients with advanced cancer were independently evaluated by 24 radiologists. The patients all had multiple serial imaging exams and had been treated with systemic therapy. Each radiologist categorized treatment responses and dictated text-based reports. Meanwhile, the AI-assisted software automatically calculated percent changes in tumor burden and categorized treatment response using standardized methods commonly found in clinical trials. A team of researchers looked for major errors such as incorrect measurements, erroneous language in reports or misidentifying the tumor location time spent in image interpretation and inter-reader agreement about the final tumor response. Twenty oncologic providers then evaluated the accuracy of the manually dictated text reports versus AI-assisted reports that included a graph, table and key images.

The AI-assisted approach increased accuracy by 25 percent, reduced major errors by 99 percent, was nearly two times faster than current practice methods, and improved inter-reader agreement by 45 percent, Smith said. The only error by the AI-assisted software was a freeform text note that we could not interpret, he noted. All of the tumor measurements, percent changes, etc., were correct. Smith, who is owner of AI Metrics as well as its CEO, did not directly participate in data gathering, have access to study data or conduct any of the statistical analysis.

Andrew Smith, M.D., Ph.D.It is gratifying to see such a practical application of artificial intelligence, said Cheri Canon, M.D., professor, chair and Witten-Stanley Endowed Chair of the Department of Radiology. Seldom do we hear of such overwhelmingly positive results from a study: 99 percent reduction in major errors. The impact this will have on patients, specifically cancer patients, will be far-reaching.

The work has other benefits, Canon noted. These include improved workflow for the radiologists, who are now more than ever burdened with complex imaging studies and increased incidence of burnout, and for our clinical colleagues, a clear and concise longitudinal report. This is a monumental improvement to the current standard of care and will in fact set a new standard.

The project involves collaborators from 21 institutions and three small businesses, including AI Metrics. The AI behind the software was trained using carefully annotated images from UAB and the National Institutes of Health by a team of UAB clinical research scientists, Smith says.

We drew freeform shapes around the edge of each tumor to train the AI to do the same, he explained. We also labelled the anatomic location of all kinds of tumors located across different parts of the body. We were able to train the AI to provide an anatomic location of the tumor. That had never been done before.

In practice, the user guides the AI, but the AI does the measuring and labeling, Smith said. We can extract the measurements in a digital form. Because the data is digital, we can generate a graph or table, and we can even save key images of all image findings. The AI-assisted reports are a major leap beyond a text-based report.

Importantly, radiologists work with the AI throughout the process, Smith says.

Lets say there is a tumor in the liver that needs to be followed over time, he said.In our AI Metrics software,theuser simply clicks on a lesion and a first AI algorithm measures it.The user can change it within two seconds if something is wrong. As you can imagine, the AI is more reliable than having different radiologists do this manually.This is what we call transparent AI, where the user both directsthe AI and can check it. Then a second AI algorithm provides the anatomic location. The user can easilycheck andcorrect this as well within about two seconds.

As part of the study, radiologists and 20 oncologic providers were asked to rate the experience of using the AI-assisted software and the value of the AI-assisted reports.

The AI-assisted software was preferred by 96 percent of radiologists, and the AI-assisted reports were preferred by 100 percent of oncologic providers, Smith said. We have established a new standard of care with AI. I think that having this software could save lives, though we dont yet have that kind of data.

Smith says the team is not done.

Since the study, we re-trained the AI on 55,000 tumors, and we hope to get closer to 100,000 in the coming months, he said. That is more tumors than an average radiologist measures in a lifetime. This is how we leverage the power of AI. The researchers are now writing an NIH grant to take their work into cancer screening and early cancer detection and management, Smith says. Most cancer therapies apply to only a few cancers or even a subtype of a single cancer. This technology applies to all solid cancers imaged on CT and MRI. We can apply this technology to many other stages of cancer.

Read an abstract of the study, Multi-institutional comparative effectiveness of advanced cancer longitudinal imaging response evaluation methods: Current practice versus artificial intelligence-assisted, on ASCOs website here.

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Artificial intelligence software improves accuracy, doubles speed in evaluating CT scans of advanced cancer - UAB News

Microsoft is cutting dozens of MSN news production workers and replacing them with artificial intelligence – Seattle Times

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By

Seattle Times staff reporter

Microsoftwont renewthe contracts fordozens of news production contractors working at MSNand plans to use artificial intelligence to replace them, several peopleclose to the situation confirmed on Friday.

The roughly 50 employees contracted through staffing agencies Aquent, IFG and MAQ Consulting were notified Wednesday that their services would no longer be needed beyond June 30.

Like all companies, we evaluate our business on a regular basis, a Microsoft spokesman said in a statement. This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic.

Full-time news producers employed by Microsoft will be retained by the company; they perform functions similar to those being let go.But all contracted news producer jobs have been eliminated.

Some employees, speaking on condition of anonymity, said MSN will use AI to replace the production work theyd been doing. That work includesusing algorithms to identify trending news stories from dozens of publishing partners and to help optimize the content by rewriting headlines or adding better accompanying photographs or slide shows.

Its been semi-automated for a few months but now its full speed ahead, one of the terminated contractors said. Its demoralizing to think machines can replace us but there you go.

Besides the production work, the contract employees also planned content, maintained the editorial calendars of partner news websites and assigned content to them.

MSN has undergone a number of changes since its launch as Microsoft Network in 1995. Once a web portal and default internet homepage for millions of personal computers, it offered original content and links to news, weather and sports.

In 2013, it rolled back original news content and began cutting employees. By 2014, it launched a redesigned version that partnered with other news sites paying them to redistribute their content.

Today, the news service relies entirely on those partnershipswith no original news content of its own. Curating stories rather than actually generating them made it easier for MSN to increasingly rely on an automated editing system, though several of the terminated employeesexpressed skepticism it will work as well with fewer human beings to monitor the technology.

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Microsoft is cutting dozens of MSN news production workers and replacing them with artificial intelligence - Seattle Times

Coronavirus tests the value of artificial intelligence in medicine – FierceHealthcare

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Albert Hsiao, M.D., Ph.D., and his colleagues at the University of California San Diego (UCSD) health system had been working for 18 months on anartificial intelligence program designed to help doctors identify pneumonia on a chest X-ray.

When thecoronavirushit the U.S., they decided to see what it could do.

The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and its providing some value in diagnosis, said Hsiao, director of UCSDs augmented imaging and artificial intelligence data analytics laboratory.

His team is one of several around the country that has pushed AI programs developed in a calmer time into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

The machine-learning programs scroll through millions of pieces of data to detect patterns that may be hard for clinicians to discern. Yet few of the algorithms have been rigorously tested against standard procedures. So while they often appear helpful, rolling out the programs in the midst of a pandemic could be confusing to doctors or even dangerous for patients, some AI experts warn.

AI is being used for things that are questionable right now, said Eric Topol, M.D., director of the Scripps Research Translational Institute and author of several books on health IT.

Topol singled out a system created by Epic, a major vendor of electronic health records software, that predicts which coronavirus patients may become critically ill. Using the tool before it has been validated is pandemic exceptionalism, he said.

RELATED:Boston startup using AI, remote monitoring to fight coronavirus

Epic said the companys model had been validated with data from more 16,000 hospitalized COVID-19 patients in 21 healthcare organizations. No research on the tool has been published, but, in any case, it was developed to help clinicians make treatment decisions and is not a substitute for their judgment, said James Hickman, a software developer on Epics cognitive computing team.

Others see the COVID-19 crisis as an opportunity to learn about the value of AI tools.

My intuition is its a little bit of the good, bad and ugly, said Eric Perakslis, Ph.D., a data science fellow at Duke University and former chief information officer at the Food and Drug Administration. Research in this setting is important.

Nearly $2 billion poured into companies touting advancements in healthcare AI in 2019. Investments in the first quarter of 2020 totaled $635 million, up from $155 million in the first quarter of 2019, according to digital health technology funderRock Health.

At least three healthcare AI technology companies have made funding deals specific to the COVID-19 crisis, including Vida Diagnostics, an AI-powered lung-imaging analysis company, according to Rock Health.

Overall, AIs implementation in everyday clinical care is less common than hype over the technology would suggest. Yet the coronavirus crisis has inspired some hospital systems to accelerate promising applications.

UCSD sped up its AI imaging project, rolling it out in only two weeks.

Hsiaos project, with research funding from Amazon Web Services, the University of California and the National Science Foundation, runs every chest X-ray taken at its hospital through an AI algorithm. While no data on the implementation has been published yet, doctors report that the tool influences their clinical decision-making about a third of the time, said Christopher Longhurst, M.D., UCSD Healths chief information officer.

The results to date are very encouraging, and were not seeing any unintended consequences, he said. Anecdotally, were feeling like its helpful, not hurtful.

RELATED:Headlines have touted AI over docs in reading medical images. New review finds evidence is limited

AI has advanced further in imaging than other areas of clinical medicine because radiological images have tons of data for algorithms to process, and more data makes the programs more effective, said Longhurst.

But while AI specialists have tried to get AI to do things like predict sepsis and acute respiratory distressresearchers at Johns Hopkins University recently won a National Science Foundation grantto use it to predict heart damage in COVID-19 patientsit has been easier to plug it into less risky areas such as hospital logistics.

In New York City, two major hospital systems are using AI-enabled algorithms to help them decide when and how patients should move into another phase of care or be sent home.

AtMount Sinai Health System, an artificial intelligence algorithm pinpoints which patients might be ready to be discharged from the hospital within 72 hours, said Robbie Freeman, vice president of clinical innovation at Mount Sinai. Freeman described the AIs suggestion as a conversation starter, meant to help assist clinicians working on patient cases decide what to do. AI isnt making the decisions.

NYU Langone Health has developed a similar AI model. It predicts whether a COVID-19 patient entering the hospital will suffer adverse events within the next four days, said Yindalon Aphinyanaphongs, M.D., Ph.D., who leads NYU Langones predictive analytics team.

The model will be run in a four- to six-week trial with patients randomized into two groups: one whose doctors will receive the alerts, and another whose doctors will not. The algorithm should help doctors generate a list of things that may predict whether patients are at risk for complications after theyre admitted to the hospital, Aphinyanaphongs said.

RELATED:Microsoft launches $40M AI for Health program to accelerate medical research

Some health systems are leery of rolling out a technology that requires clinical validation in the middle of a pandemic. Others say they didnt need AI to deal with the coronavirus.

Stanford Health Careis not using AI to manage hospitalized patients with COVID-19, saidRon Li, M.D., the centers medical informatics director for AI clinical integration. The San Francisco Bay Area hasnt seen the expected surge of patientswho would have provided the mass of data needed to make sure AI works on a population, he said.

Outside the hospital, AI-enabled risk factor modeling is being used to help health systems track patients who arent infected with the coronavirus but might be susceptible to complications if they contract COVID-19.

At Scripps Health in San Diego, clinicians are stratifying patients to assess their risk of getting COVID-19 and experiencing severe symptoms using a risk-scoring model that considers factors like age, chronic conditions and recent hospital visits. When a patient scores 7 or higher, a triage nurse reaches out with information about the coronavirus and may schedule an appointment.

Though emergencies provide unique opportunities to try out advanced tools, its essential for health systems to ensure doctors are comfortable with them, and to use the tools cautiously, with extensive testing and validation, Topol said.

When people are in the heat of battle and overstretched, it would be great to have an algorithm to support them, he said. We just have to make sure the algorithm and the AI tool isnt misleading, because lives are at stake here.

ThisKHNstory first published onCalifornia Healthline, a service of theCalifornia Health Care Foundation.Kaiser Health Newsis a nonprofit news service covering health issues. It is an editorially independent program of the Kaiser Family Foundation, which is not affiliated with Kaiser Permanente.

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Coronavirus tests the value of artificial intelligence in medicine - FierceHealthcare

The Use of Artificial Intelligence by Investment Advisers: Considerations Based on an Advisers Fiduciary Duties – JD Supra

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Artificial intelligence (AI) is an increasingly important technology within the investment management industry.1AI has been used in a variety of waysincluding as the newest strategy for attempts to "beat the market" by outperforming passive index funds that are benchmarked against the S&P 500, despite the long-standing finding that index funds consistently win that contest.2

Investment advisers who use AI should consider the unique issues the technology raises in light of an adviser's fiduciary duty to its clients. In this client alert, we provide an overview of how AI is being used by investment advisers, the fiduciary duties applicable to investment advisers, and particular issues advisers should consider in designing AI-based programs, to ensure they are acting in the best interests of their clients.3

How Artificial Intelligence Is Being Adopted by Investment Advisers

AI is currently used by investment advisers in a variety of innovative ways:

Issues Raised by an Investment Adviser's Fiduciary Duties

Under federal law, an investment adviser is a fiduciary to its clients.8An adviser's fiduciary duty involves a duty of care and a duty of loyalty, which, although not defined specifically in the Investment Advisers Act of 1940 (Advisers Act), have been addressed and developed through U.S. Securities and Exchange Commission (SEC) interpretive releases and guidance, as well as case law.9As discussed below, these duties have implications for an adviser's use of AI. The specific obligations required by an adviser's fiduciary duty will depend upon what functions the adviser has agreed to assume for the client.10While the SEC has not provided specific guidance for advisers using AI, current guidance raises unique considerations for advisers to consider.11

Duty of Loyalty

The duty of loyalty requires investment advisers not to place their own interest ahead of their clients' interests.12An adviser must make full and fair disclosure to its client of all material facts relating to the advisory relationship and employ reasonable care to avoid misleading clients. Information provided to clients must be sufficiently specific so that a client is able to understand the investment adviser's business practices and conflicts of interest.

An adviser's duty of loyalty raises, among others, the following issues with respect to AI-based investment management programs:

What facts does an adviser need to disclose about its use of AI?

Advisers should consider disclosing information such as the following:

How should advisers think about the tension between disclosure obligations and confidentiality regarding proprietary technologies?

It is important for advisers to disclose enough information for investors to make an informed decision about engaging, and then managing the relationship with, the investment adviser. Advisers should be careful to not mislead clients, and information provided to clients should be sufficiently specific so that a client is able to understand the investment adviser's business practices. However, highly technical information about the process behind the AI's decisions might not be beneficial to a client's understanding of the adviser's platform.

Does an adviser need to disclose the historical success rate of returns from using artificial intelligence?

Historically, funds that employ AI have not outperformed the S&P 500.13Investment advisers might therefore be expected to provide disclosures indicating that an adviser has not conclusively proven AI's ability to predict securities prices and may not "beat the market."14

Duty of Care

The duty of care requires, among other things, the duty to provide advice appropriate for the client and the duty to monitor a client's investments, and the ongoing suitability of those investments, over the course of the relationship.15An adviser must develop a reasonable understanding of the client's objectives and have a reasonable belief that the advice it provides is in the best interest of the client, based on the client's portfolio and objectives.

An adviser's duty of care raises, among others, the following issues with respect to AI-based investment management programs:

Can an adviser replace traditional suitability assessments with alternative data or other AI-based tools?

If an AI-based system makes investment choices on behalf of clients using deep or machine learning that develops on its own, by tracking client behavior, or by using alternative data, the adviser should pay particularly close attention to how the recommendations generated by those data might differ or be in conflict with a client's explicit preferences and investment objectives. It is possible that an adviser using AI-based tools will make different assessments of what is best or appropriate for the client than if the adviser uses more traditional tools like suitability questionnaires that ask a client about her risk profile, investment objectives, and other characteristics. As a result, an adviser using AI-based systems to generate an investment management system may be at cross-purposes with the client, which would raise issues based on the adviser's duty of care.

How frequently should an investment adviser evaluate its AI program?

Because AI programs create their own rules based on the data they analyze, and autonomously make trading decisions, advisers should develop internal procedures for ensuring their programs are operating correctly.16For example, advisers should adequately test their AI before and periodically after it is integrated into the investment platform. In addition, advisers should develop strategies for procedures they can implement to adjust their AI programs if they do not produce favorable results. Advisers should also monitor for possible cybersecurity threats.

How should an adviser review investment decisions directed by AI to ensure the decisions still fit within a client's investment goals?

Advisers using AI should adopt and implement procedures that will periodically review the performance of their AI, to ensure that performance is within expected parameters and that decisions are not being made to the detriment of clients' investment goals. Ultimately, the adviser is responsible for all decisions made by its AI-based program and therefore cannot let an AI-based program simply run without the adviser's active monitoring.17

[1] For example, on May 26, 2020, HSBC announced the launch of its AI Powered US Equity Index family, the first equity index product powered by artificial intelligence and big data, using data insights from IBM Watson to guide equity trading decisions. HSBC Launches First Equity Index Products Powered by AI and Big Data, Business Wire (May 26, 2020), https://www.businesswire.com/news/home/20200526005556/en/HSBC-Launches-Equity-Index-Products-Powered-AI.

[2] In his 1972 book A Random Walk Down Wall Street, Burton Malkiel maintains that share prices evolve similarly to a random walk and have no relationship with historic values or other variables. Because future share prices are not based on any pattern, they cannot be predicted, and it is unlikely any random fund adviser will be able to out-perform a passive investment fund. Burton Malkiel, A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing. W.W. Norton, 1972. Since the 1972 publication, research continues to show that beating the market is unlikely. For example, a study released at the end of 2019 indicated that over the past 10 years, 89 percent of large-cap funds, 84 percent of mid-cap funds, and 89 percent of small-cap funds failed to outperform their S&P 500 benchmark on a relative basis. SPIVA U.S. Year-End 2019 Scorecard, S&P Dow Jones Indices (2019), https://us.spindices.com/indexology/core/spiva-us-year-end-2019.

[3] Of course, the federal securities laws raise issues that are important for investment advisers who employ AI to consider even outside their fiduciary duties. For example, advisers using AI may want to rely on Rule 3a-4 under the Investment Company Act of 1940, which provides a safe harbor from registration as an investment company for advisory programs that are reasonably customized to particular investors, with respect to their investment programs. If an AI-based program provides similar advice to a variety of clients, however, the adviser may not be able to rely on Rule 3a-4. Thus, advisers who manage client accounts based on AI may need to consider the specific contours of their AI programs in light of the requirements of Rule 3a-4. This alert by no means covers the range of issues AI models raise for investment advisers.

[4] Adam Satariano and Nishant Kumar, The Massive Hedge Fund Betting on AI, Bloomberg (Sept. 27, 2017), https://www.bloomberg.com/news/features/2017-09-27/the-massive-hedge-fund-betting-on-ai.

[5] Adam Satariano and Nishant Kumar, The Massive Hedge Fund Betting on AI, Bloomberg (Sept. 27, 2017), https://www.bloomberg.com/news/features/2017-09-27/the-massive-hedge-fund-betting-on-ai.

[6] Robo-advisers are online platforms, and typically registered investment advisers, that provide discretionary asset management services to their clients through online algorithmic-based programs. See Division of Investment Management, Robo Advisers, IM Guidance Update No. 2017-02 (Feb. 2017).

[7] See, e.g., Ryan W. Neal, Wealthfront Turns to Artificial Intelligence to Improve Robo Advice (Mar. 31, 2016), https://www.wealthmanagement.com/technology/wealthfront-turns-artificial-intelligence-improve-robo-advice.

[8] SEC v. Capital Gains Research Bureau, Inc., 375 U.S. 180, 194 (1963). An investment advisers fiduciary duty is imposed under the Advisers Act in recognition of the relationship of trust between an investment adviser and a client and is made enforceable by the antifraud provisions of Section 206 of the Advisers Act.

[9] See, e.g., Capital Gains 375 U.S. at 194; SEC v. Tambone, 550 F.3d 106, 146 (1st Cir. 2008); SEC v. Moran, 944 F. Supp. 286, 297 (S.D.N.Y 1996); Transamerica Mortgage Advisors, Inc. v. Lewis, 444 U.S. 11, 17 (1979); Commission Interpretation Regarding Standard of Conduct for Investment Advisers, Investment Advisers Act Release No. 5248 (July 12, 2019); IM Guidance Update No. 2017-02 (Feb. 2017); Proxy Voting by Investment Advisers, Investment Advisers Act Release No. 2106 (Jan. 31, 2003). See also SEC Commissioner Kara M. Stein, Surfing the Way: Technology, Innovation, and Competition, Remarks at Harvard Law Schools Fidelity Guest Lecture Series (No. 9, 2015), available at https://www.sec.gov/news/speech/surfing-wave-technology-innovation-and-competition-remarks-harvard-law-schools-fidelity (former SEC Commissioner Stein discussing the application of fiduciary duties to digital advice provided by robo-advisers).

[10] See, e.g., Investment Advisers Act Release No. 5248) (in interpretive guidance related to investment advisers fiduciary duties, explaining that while all investment advisers owe their clients a fiduciary duty, that fiduciary duty must be viewed in the context of the agreed-upon scope of the relationship between the adviser and the client; the fiduciary duty itself may not be waived, but the exact responsibilities of the adviser in managing the clients account may be limited).

[11] Although not directly on point, guidance released by the SECs Division of Investment Management with respect to robo-advisers provides some light on the types of considerations advisers should address based on their fiduciary duties to clients when adopting non-traditional methods of investment advisory services. IM Guidance Update No. 2017-02 (Feb. 2017). Additionally, former SEC personnel have spoken on the SECs own use of AI. See, e.g., SEC Commissioner Kara M. Stein, From the Data Rush to the Data Wars: A Data Revolution in Financial Markets (Sept. 27, 2018), available at https://www.sec.gov/news/speech/speech-stein-092718; Scott W. Bauguess, Acting Director and Acting Chief Economist, SEC Division of Economic and Risk Analysis, The Role of Big Data, Machine Learning, and AI in Assessing Risks: a Regulatory Perspective (June 21, 2017), available at https:// http://www.sec.gov/news/speech/bauguess-big-data-ai.

[12] Investment Advisers Act Release No. 5248.

[13] Mark Hulbert, Using AI for Picking Stocks? Not So Fast, Wall Street Journal (Jan 5, 2020), https://www.wsj.com/articles/use-ai-for-picking-stocks-not-so-fast-11578279960.

[14] In a response to a request for a no action letter, the staff of the SEC explained to an investment adviser purporting to rely on abilities as a psychic medium that he would be required to disclose that the predictive value of his methods had not been scientifically established. John Anthony, SEC Staff No Action Letter (Mar. 19, 1975). Similarly, because the predictive value of AI has not been conclusively established, investment advisers should consider the extent to which the SEC would expect them to disclose the lack of proof that AI-based algorithms are better than or even equal to the success of more traditional tools.

[15] Investment Advisers Act Release No. 5248.

[16] The Financial Industry Regulatory Authority (FINRA) published guidance in 2016 related to the governance and supervision framework advisers should have in place to adopt client-facing digital investment advice tools. While not directly related to AI, the guidance emphasizes the importance of supervising algorithms used in digital-advice tools and periodically assessing whether [the] algorithm is consistent with the firms investment and analytical approaches. See FINRA Report on Digital Investment Advice (Mar. 2016), available at https://www.finra.org/sites/default/ files/digital-investment-advice-report.pdf.

The SEC has previously brought enforcement actions against investment advisers for failing to supervise those in charge of programming and monitoring algorithmic or other automated trading strategies. In one enforcement action, the founder of a hedge fund allowed his co-founder complete control of operating and monitoring an algorithm to make trade decisions. He was made aware that the algorithm was not working as expected and made no efforts to follow-up with the co-founder or inform investors or prospective investors of any issues related to the algorithm. In the Matter of Timothy S. Dembski, Investment Advisers Act Release No. 4671 (March 24, 2017).

[17] For example, robo-adviser Wealthfront was the subject of an SEC enforcement action for providing a false statement that it monitored all client accounts to avoid transactions that might result in wash sale, which would potentially reduce investor returns, when it did not actually do so. See In the Matter of Wealthfront Advisers, Investment Advisers Act Release No. 5086 (Dec. 21, 2018).

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The Use of Artificial Intelligence by Investment Advisers: Considerations Based on an Advisers Fiduciary Duties - JD Supra

Lecturer in Artificial Intelligence for Digital Infrastructures job with UNIVERSITY OF BRISTOL | 208485 – Times Higher Education (THE)

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Job number ACAD104575Division/School School of Computer Science, Electrical and Electronic Engineering and Engineering MathsContract type Open EndedWorking pattern Full timeSalary 38,017 - 49,553Closing date for applications 28-Jun-2020

The Smart Internet Lab at the University of Bristol is one of the UK's most renowned Communications and Networks research centres aiming to address grand technological, societal and industrial challenges. Our 200 experts on wireless, optical communications and networks challenge the complexity of tomorrow's world by fusing research expertise and innovation in a range of research areas such as: IoT, 5G/6G, Future Internet, Autonomous Networks, Machine Learning, Artificial Intelligence, Network Convergence, Mobile Edge Computing and Network Softwarization. Our unique offering across optical, wireless, IoT and cloud technologies enable us to bring together end-to-end network design and optimisation and impact regional, national and global ICT innovations.

BDFI is a University Research Institute that pioneers cross-disciplinary approaches to digital innovation. BDFI is developing in-depth understanding of sociotechnical insights to drive the creation of digital technologies for inclusive, prosperous and sustainable societies. The Institute has recently received 110m funding from UKRI, the private sector and philanthropy to develop a set of unique research facilities to fulfil its mission.

This post will address both physical & virtual elements, connectivity and cloud. Experience in the areas of AI for telecom infrastructure, AI for network computing, AI as a Service, Knowledge as a Service, network informatics and data analytics, resource abstraction and resource management, software control and autonomous operations is highly desirable. Many of these subjects are critical in the development of Smart Cities, Smart Manufacturing and Smart Utilities. Both the Smart Internet Lab and BDFI have a number of upcoming research projects where the research remit of this academic post are well aligned, so the successful applicant will be joining a vibrant and active cross-disciplinary research environment. Successful applicants will have a proven track record in high quality teaching at undergraduate and postgraduate levels.

Please include with your CV and covering letter with a statement on the contributions that you can make to teaching in the Department, especially in respect of innovation in delivery and content. We would like to encourage applications from groups under-represented in electronic engineering.

For informal discussion about the post, you are welcome to contact:

Professor Angela Doufexi (mvse-eee@bristol.ac.uk), (Head of Department) or

Professor Ian Nabney (sceem-hos@bristol.ac.uk), (Head School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths) or

Professor Dimitra Simeonidou (Dimitra.Simeonidou@bristol.ac.uk) (Director of Smart Internet Lab and co-Director of BDFI)

The selection process, including interviews is expected to take place in June/July 2020.

We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ andBAME communities, to join us.

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Lecturer in Artificial Intelligence for Digital Infrastructures job with UNIVERSITY OF BRISTOL | 208485 - Times Higher Education (THE)

Artificial Intelligence (AI) Market to Reach USD 202.57 Billion by 2026; Rising Demand for Cloud-based Applications to Aid Growth: Fortune Business…

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Pune, May 25, 2020 (GLOBE NEWSWIRE) -- The global AI market is set to gain momentum from the rising utilization of cloud-based services and applications worldwide. Also, the increasing adoption of connected devices would impact the market positively in the coming years. This information is published by Fortune Business Insights in a recent report, titled, Artificial Intelligence (AI) Market Size, Share and Industry Analysis By Component (Hardware, Software, Services), By Technology (Computer Vision, Machine Learning, Natural Language Processing, Others), By Industry Vertical (BFSI, Healthcare, Manufacturing, Retail, IT & Telecom, Government, Others) and Regional Forecast, 2019-2026. The report further states that the global AI market size stood at USD 20.67 billion in 2018 and is projected to reach USD 202.57 billion by 2026, thereby exhibiting a CAGR of 33.1% during the forecast period.

Highlights of This Report:

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An Overview of the Impact of COVID-19 on this Market:

The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

Click here to get the short-term and long-term impact of COVID-19 on this Market.

Please visit: https://www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-market-100114

Drivers & Restraints-

Rising Demand for Industrial Robots to Propel Growth

The rising demand for customized robots is a vital driver of the AI market growth. Numerous reputed organizations in the developed nations are presently engaging in the development and supply of industrial robots equipped with the AI technology. Japan and South Korea, for instance, supplied approximately 38,600 and 41,400 units of industrial robots in 2016, respectively. Also, in the same year, China provided almost 87,000 units across the globe. Apart from that, AI technology is mainly required in the retail sector for enhancing customer service. Coupled with this, the increasing usage of machine learning (M2P and M2M) would contribute to the market growth. However, the rising concerns regarding the unreliability of AI algorithms and data privacy may hamper the market growth.

Segment-

Natural Language Processing Segment to Dominate Owing to Its Usage in Various Applications

In terms of technology, the market is segregated into natural language processing, machine learning, computer vision, and others. Amongst these, the computer vision segment held 22.5% AI market share in 2018. This system helps in identifying and detecting patterns. It also synthesizes, analyses, and acquires realistic interactive interfaces. Then, it utilizes the ID tags to showcase pictures of associated items. The natural language processing segment currently accounts of the maximum share as it is adopted for a wide range of applications, such as Informational Retrieval (IR), speech processing, semantic disambiguation, text parsing, and machine translation.

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Regional Analysis-

Rising Adoption of AI by Biopharma Companies to Favor Growth in Asia Pacific

In 2018, North America procured USD 9.72 billion revenue and is set to remain in the leading position throughout the forecast period. This growth is attributable to the ongoing technological advancements in the fields of natural language processing, machine learning, and analytical tools. Besides, the rising awareness programs regarding the benefits of AI tools and systems would propel growth in this region. Asia Pacific, on the other hand, is expected to grow considerably backed by the major contribution of China. The government of this country is planning to merge with Baidu to support the implementation of AI and develop a deep learning laboratory consisting of military, manufacturing, smart agriculture, and intelligent logistics. Apart from that, AI is being extensively adopted by a large number of biopharma companies in this region. Developed nations, such as Japan are investing hefty amounts of money in creating AI algorithms to analyze large volumes of data.

Competitive Landscape-

Key Players Focus on Launching New Products to Strengthen Position

The market is fragmented with various companies operating across the world. They are mainly focusing on investing huge sums to develop new products. Numerous start-ups are adopting the strategy of mergers and acquisitions. Some of the others are considering the impact of the outbreak of Covid-19 pandemic and are making novel solutions to help people in performing various tasks. Below are a couple of the recent industry developments:

Fortune Business Insights lists out the names of all the AI service providers present in the global market. They are as follows:

Quick Buy AI Market Research Report https://www.fortunebusinessinsights.com/checkout-page/100114

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Identity And Access Management Market Size, Share and Industry Analysis By Component (Provisioning, Directory Services, Single Sign-On, Others), By Deployment Model (Cloud, On-Premises), By Enterprise Size (Large Enterprises, Small and Medium Enterprises), By Industry Vertical (BFSI, IT and Telecom, Retail and Consumer Packed Goods, Others) And Regional Forecast 2019-2026

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Artificial Intelligence (AI) Market to Reach USD 202.57 Billion by 2026; Rising Demand for Cloud-based Applications to Aid Growth: Fortune Business...

Walmart Employees Are Out to Show Its Anti-Shoplifting AI Doesn’t Work – WIRED

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In January, my coworker received a peculiar email. The message, which she forwarded to me, was from a handful of corporate Walmart employees calling themselves the Concerned Home Office Associates. (Walmarts headquarters in Bentonville, Arkansas, is often referred to as the Home Office.) While its not unusual for journalists to receive anonymous tips, they dont usually come with their own slickly produced videos.

The employees said they were past their breaking point with Everseen, a small artificial intelligence firm based in Cork, Ireland, whose technology Walmart began using in 2017. Walmart uses Everseen in thousands of stores to prevent shoplifting at registers and self-checkout kiosks. But the workers claimed it misidentified innocuous behavior as theft, and often failed to stop actual instances of stealing.

They told WIRED they were dismayed that their employerone of the largest retailers in the worldwas relying on AI they believed was flawed. One worker said that the technology was sometimes even referred to internally as NeverSeen because of its frequent mistakes. WIRED granted the employees anonymity because they are not authorized to speak to the press.

The workers said they had been upset about Walmarts use of Everseen for years, and claimed colleagues had raised concerns about the technology to managers, but were rebuked. They decided to speak to the press, they said, after a June 2019 Business Insider article reported Walmarts partnership with Everseen publicly for the first time. The story described how Everseen uses AI to analyze footage from surveillance cameras installed in the ceiling, and can detect issues in real time, such as when a customer places an item in their bag without scanning it. When the system spots something, it automatically alerts store associates.

Everseen overcomes human limitations. By using state-of-the-art artificial intelligence, computer vision systems, and big data we can detect abnormal activity and other threats, a promotional video referenced in the story explains. Our digital eye has perfect vision and it never needs a day off.

In an effort to refute the claims made in the Business Insider piece, the Concerned Home Office Associates created a video, which purports to show Everseens technology failing to flag items not being scanned in three different Walmart stores. Set to cheery elevator music, it begins with a person using self-checkout to buy two jumbo packages of Reeses White Peanut Butter Cups. Because theyre stacked on top of each other, only one is scanned, but both are successfully placed in the bagging area without issue.

The same person then grabs two gallons of milk by their handles, and moves them across the scanner with one hand. Only one is rung up, but both are put in the bagging area. They then put their own cell phone on top of the machine, and an alert pops up saying they need to wait for assistancea false positive. Everseen finally alerts! But does so mistakenly. Oops again, a caption reads. The filmmaker repeats the same process at two more stores, where they fail to scan a heart-shaped Valentines Day chocolate box with a puppy on the front and a Philips Sonicare electric toothbrush. At the end, a caption explains that Everseen failed to stop more than $100 of would-be theft.

False Positives

The video isnt definitive proof that Everseens technology doesnt work as well as advertised, but its existence speaks to the level of frustration felt by the group of anonymous Walmart employees, and the lengths they went to prove their objections had merit.

In interviews, the workers, whose jobs include knowledge of Walmarts loss prevention programs, said their top concern with Everseen was false positives at self-checkout. The employees believe that the tech frequently misinterprets innocent behavior as potential shoplifting, which frustrates customers and store associates, and leads to longer lines. Its like a noisy tech, a fake AI that just pretends to safeguard, said one worker.

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Walmart Employees Are Out to Show Its Anti-Shoplifting AI Doesn't Work - WIRED

How artificial intelligence is keeping time-critical shipments on track during pandemic – FreightWaves

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Consumers are seeing and feeling the impact of COVID-19 supply chain interruptions and delays in their everyday lives, from shortages of paper goods and cleaning supplies in grocery stores, to rising prices for beef and poultry.

For specialized industries such as health care and aerospace, however, the stakes of supply chain interruptions and service failures have perhaps never been higher. So far the traditional hub-and-spoke time-critical logistics industry has largely struggled to adapt, while newer technology-enabled models in the industry are showing significant promise to perform in a crisis.

Artificial intelligence (AI) platforms in particular have shown remarkable resilience during the COVID-19 crisis and the ability to quickly pivot shipments with minimal delays and service failures. California-based Airspace Technologies was one of the first logistics providers in the time-critical space to implement a breakthrough AI-powered platform that they say has enabled them to swiftly adjust operations without interruptions to their 24/7, 365-days-a-year services.

Airspace was built with moments like these in mind. It was designed to perform in a crisis when time is of the essence and lives and entire industries are quite literally on the line, said Airspace Technologies CEO and co-founder Nick Bulcao.

With years of experience specializing in urgent medical deliveries, such as organs for transplant, as well as aerospace parts for downed aircraft, Airspace says they have noticed a significant impact on their business as elective surgeries are delayed and less aircraft are flying. But the automated, AI-driven software that is the heartbeat of their operations has made adjusting to the new realities of the industry immensely more manageable.

With lives on the line, Airspace moved quickly to set up new shipment networks and routes each day to begin transporting urgently needed COVID-19 test kits, blood and plasma units, and vital organs for transplant to get where they need to go. Their fully transparent, automated software platform also allows minute-by-minute real-time tracking of deliveries, so hospitals and labs know exactly where kits or urgent supplies are and when they will arrive.

Airspace is currently making between 250 and 300 health care-related deliveries each day, and has transported as many as 30 organs in just one week.

The companys aerospace parts delivery business has had its own heroic moments during the COVID-19 crisis. An independent delivery driver for Airspace in the Bay Area recounted a harrowing incident last month in which he was asked to make a critical aerospace part delivery not to an airport, but to Stanford University Medical Center instead. Sensing the urgency of the moment, the driver immediately retrieved the part and made his way to the hospital.

Arriving two hours earlier than expected, I called my point of contact, who was still over an hour away. After some coordination with the engineer and hospital staff, I handed over the critical part for the medevac helicopter stranded on the hospital roof to a nurse instead helping get the lifesaving equipment back in the air ahead of schedule, said Bryan Sperry, 61, the driver.

Airspace says software also allowed them to protect workers by rapidly transitioning their team to fully remote operations across the United States.

The key was doing so with zero disruption to our round-the-clock operations and with full capabilities still in place, said Ryan Rusnak, Airspace co-founder and chief technology officer. After some planning, it took the team less than 36 hours to make a complete transition. Theyre now remotely continuing to provide the seamless, end-to-end experience our customers expect.

The transition and dramatic decline in passenger flights has not been without its challenges, though. Fewer passenger flights means fewer routing options, often accompanied by delays that can be costly for customers. That is where the power of the AI platform can often make the biggest difference, Airspace says.

One of the key features of their AI software is an automated delay declaration, which allows the operations team to quickly pivot to the next optimal routing if an order experiences a flight delay even in the middle of a trip. For example, on one day in March this year, amid more than 100 flight cancellations at the Las Vegas airport, Airspaces technology allowed the company to reduce disruption to critical deliveries to less than 38-minute average delays, while over 60% of orders there experienced no delays at all.

The rapidly changing dynamics as a result of the COVID-19 pandemic have created enormous challenges across industries and supply chains, but the power of AI to keep industry and lifesaving goods and services moving in a crisis has shown a positive path toward maintaining affordability, speed, reliability and transparency in urgent logistics.

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How artificial intelligence is keeping time-critical shipments on track during pandemic - FreightWaves