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Global Machine Learning Courses Market Research Report 2015-2027 of Major Types, Applications and Competitive Vendors in Top Regions and Countries -...

Artificial intelligence can be used to diagnose cancer, predict suicide, and assist in surgery. In all these cases, studies suggest AI outperforms human doctors in set tasks. But when something does go wrong, who is responsible?

Theres no easy answer, says Patrick Lin, director of Ethics and Emerging Sciences Group at California Polytechnic State University. At any point in the process of implementing AI in healthcare, from design to data and delivery, errors are possible. This is a big mess, says Lin. Its not clear who would be responsible because the details of why an error or accident happens matters. That event could happen anywhere along the value chain.

Design includes creation of both hardware and software, plus testing the product. Data encompasses the mass of problems that can occur when machine learning is trained on biased data, while deployment involves how the product is used in practice. AI applications in healthcare often involve robots working with humans, which further blurs the line of responsibility.

Responsibility can be divided according to where and how the AI system failed, says Wendall Wallace, lecturer at Yale Universitys Interdisciplinary Center for Bioethics and the author of several books on robot ethics. If the system fails to perform as designed or does something idiosyncratic, that probably goes back to the corporation that marketed the device, he says. If it hasnt failed, if its being misused in the hospital context, liability would fall on who authorized that usage.

Surgical Inc., the company behind the Da Vinci Surgical system, has settled thousands of lawsuits over the past decade. Da Vinci robots always work in conjunction with a human surgeon, but the company has faced allegations of clear error, including machines burning patients and broken parts of machines falling into patients.

Some cases, though, are less clear-cut. If diagnostic AI trained on data that over-represents white patients then misdiagnoses a Black patient, its unclear whether the culprit is the machine-learning company, those who collected the biased data, or the doctor who chose to listen to the recommendation. If an AI program is a black box, it will make predictions and decisions as humans do, but without being able to communicate its reasons for doing so, writes attorney Yavar Bathaee in a paper outlining why the legal principles that apply to humans dont necessarily work for AI. This also means that little can be inferred about the intent or conduct of the humans that created or deployed the AI, since even they may not be able to foresee what solutions the AI will reach or what decisions it will make.

The difficulty in pinning the blame on machines lies in the impenetrability of the AI decision-making process, according to a paper on tort liability and AI published in the AMA Journal of Ethics last year. For example, if the designers of AI cannot foresee how it will act after it is released in the world, how can they be held tortiously liable?, write the authors. And if the legal system absolves designers from liability because AI actions are unforeseeable, then injured patients may be left with fewer opportunities for redress.

AI, as with all technology, often works very differently in the lab than in a real-world setting. Earlier this year, researchers from Google Health found that a deep-learning system capable of identifying symptoms of diabetic retinopathy with 90% accuracy in the lab caused considerable delays and frustrations when deployed in real life.

Despite the complexities, clear responsibility is essential for artificial intelligence in healthcare, both because individual patients deserve accountability, and because lack of responsibility allows mistakes to flourish. If its unclear whos responsible, that creates a gap, it could be no one is responsible, says Lin. If thats the case, theres no incentive to fix the problem. One potential response, suggested by Georgetown legal scholar David Vladeck, is to hold everyone involved in the use and implementation of the AI system accountable.

AI and healthcare often work well together, with artificial intelligence augmenting the decisions made by human professionals. Even as AI develops, these systems arent expected to replace nurses or automate human doctors entirely. But as AI improves, it gets harder for humans to go against machines decisions. If a robot is right 99% of the time, then a doctor could face serious liability if they make a different choice. Its a lot easier for doctors to go along with what that robot says, says Lin.

Ultimately, this means humans are ceding some authority to robots. There are many instances where AI outperforms humans, and so doctors should defer to machine learning. But patient wariness of AI in healthcare is still justified when theres no clear accountability for mistakes. Medicine is still evolving. Its part art and part science, says Lin. You need both technology and humans to respond effectively.

Read more here:
When AI in healthcare goes wrong, who is responsible? - Quartz

Twitter it was looking into why the neural network it uses to generate photo previews apparently chooses to show white peoples faces more frequently than Black faces.

Several Twitter users demonstrated the issue over the weekend, posting examples of posts that had a Black persons face and a white persons face. Twitters preview showed the white faces more often.

The informal testing began after a Twitter user tried to post about a problem he noticed in Zooms facial recognition, which was not showing the face of a Black colleague on calls. When he posted to Twitter, he noticed it too was favoring his white face over his Black colleagues face.

Users discovered the preview algorithm chose non-Black cartoon characters as well.

When Twitter first began using the neural network to automatically crop photo previews, machine learning researchers explained in a blog post how they started with facial recognition to crop images, but found it lacking, mainly because not all images have faces:

Previously, we used face detection to focus the view on the most prominent face we could find. While this is not an unreasonable heuristic, the approach has obvious limitations since not all images contain faces. Additionally, our face detector often missed faces and sometimes mistakenly detected faces when there were none. If no faces were found, we would focus the view on the center of the image. This could lead to awkwardly cropped preview images.

Twitter chief design officer Dantley Davis tweeted that the company was investigating the neural network, as he conducted some unscientific experiments with images:

Liz Kelley of the Twitter communications team tweeted Sunday that the company had tested for bias but hadnt found evidence of racial or gender bias in its testing. Its clear that weve got more analysis to do, Kelley tweeted. Well open source our work so others can review and replicate.

Twitter chief technology officer Parag Agrawal tweeted that the model needed continuous improvement, adding he was eager to learn from the experiments.

Read more:
Twitter is looking into why its photo preview appears to favor white faces over Black faces - The Verge

In its latest keynote address headed by CEO Tim Cook, Apple its new A14 bionic chip, a 5 nm ARM based chipset.

This System on a Chip (SoC) from Apple is expected to power iPhone 12 and iPad Air (2020) models. The chipset integrates around 11.8 billion transistors.

For over a decade, Apples world-class silicon design team has been building and refining Apple SoCs. Using these designs Apple has been able to develop the latest iPhone, iPad and Apple Watch that are the industry leaders in terms of class and performance. In June of 2020, Apple announced that it will transition the Mac to its custom silicon to offer better technological performance.

Now, Apple Silicon is basically a processor made in-house akin to what is powering the iPhone and iPad family of devices. This ARM move will result in ditching their reliance on Intel chipsets for Future Macs. This transition to silicon will also establish a common architecture across all Apple products, making it far easier for developers to write and optimize their apps for the entire ecosystem. In fact, developers can now start focusing on updating their applications to take advantage of the enhanced capabilities of the Apple silicon.

Along with this Apple also introduced mac0S Big Sur earlier this year, which will be the next major macOS release (version 11.0) and includes technologies that will facilitate a smooth transition to the Apple silicon experience. This will be the first time where developers will be able to make their iOS and iPad OS apps available on the Mac without modifications. The Apple silicon powered Macs will offer industry leading performance per watt and higher performance GPUs. To help developers get accustomed to the new transition, Apple is also launching the Universal App QuickStart Program to guide developers through the entire transition.

Apple plans to ship the new Mac by the end of the year and complete the transition in about two years. This being said Apple will continue to release new versions for Intel-based Mac for years to come.

Apple has been explicit about how serious they are about machine learning-based SoC. Apple A14 includes second-generation machine learning accelerators in the CPU for 10 times faster machine learning calculations. The combination of the new Neural Engine, machine learning accelerators, advanced power management, unified memory architecture and the Apple high-performance GPU enables powerful on-device experiences for image recognition, natural language learning, analysing motion, and maybe a machine learning enabled GPS!

According to a recent patent application by Apple , they have been working on a technology that implements a system for estimating the device location based on a global positioning system consisting of a Global Navigation Satellite System (GNSS) satellite, and receives a set of parameters associated with the estimated position. The processor is further configured to apply the set of parameters and the estimated position to a machine learning model that has been trained on a position relative to the satellite. The estimated position and output of the model is then provided to a Kalman filter for more accurate location.

This technology may be significantly better than what a mobile device alone can perform in most non-aided mode(s) of operation. Apples patent to improve GPS in the upcoming 5G era might give them an advantage over existing resources.

Apples move to its own ARM chips comes just as the company unveils macOS version 11.0 (Big Sur). That means ARM based Mac computers will continue to run macOS instead of switching to iOS 14, similar to the approach taken with existing Windows laptops that use Qualcomm ARM based processors. Apple apparently has its hardware and software team working together, given that they have found a way for all their applications functioning seamless from day one of the launch, through Rosetta 2 acting as an emulator and a translator that will allow Intel-made apps to run on Silicon-powered devices.

Moreover, the Apple ecosystem acts as the catalyst for innovation in the company and is not limited to the hardware and software products, but also around its services.

Putting a foot forward in that direction is the Apple One Subscription.

Apple with its calm dignity, diligent market study and unflinching courage to innovate has taken its own time to come up with their strategic silicon move. Apple stayed focused on its long term goals instead of following the hype, trends and gimmicks set out by its competitors to gain customer attention. This ability to think differently is a driving force behind their success.

And owing to the current state of affairs Apple has played it relatively safe this year, sticking to their core offerings. We can expect an exciting iPhone, iMac and MacOS launch later this year.

Lets gear up for another round of innovation sponsored by Apple.

See original here:
Solving the crux behind Apple's Silicon Strategy - Medium

An unlikely scandal engulfed the British government last month. After COVID-19 forced the government to cancel the A-level exams that help determine university admission, the British education regulator used an algorithm to predict what score each student would have received on their exam. The algorithm relied in part on how the schools students had historically fared on the exam. Schools with richer children tended to have better track records, so the algorithm gave affluent students even those on track for the same grades as poor students much higher predicted scores. High-achieving, low-income pupils whose schools had not previously performed well were hit particularly hard. After threats of legal action and widespread demonstrations, the government backed down and scrapped the algorithmic grading process entirely. This wasnt an isolated incident: In the United States, similar issues plagued the International Baccalaureate exam, which used an opaque artificial intelligence system to set students' scores, prompting protests from thousands of students and parents.

These episodes highlight some of the pitfalls of algorithmic decision-making. As technology advances, companies, governments, and other organizations are increasingly relying on algorithms to predict important social outcomes, using them to allocate jobs, forecast crime, and even try to prevent child abuse. These technologies promise to increase efficiency, enable more targeted policy interventions, and eliminate human imperfections from decision-making processes. But critics worry that opaque machine learning systems will in fact reflect and further perpetuate shortcomings in how organizations typically function including by entrenching the racial, class, and gender biases of the societies that develop these systems. When courts and parole boards have used algorithms to forecast criminal behavior, for example, they have inaccurately identified Black defendants as future criminals more often than their white counterparts. Predictive policing systems, meanwhile, have led the police to unfairly target neighborhoods with a high proportion of non-white people, regardless of the true crime rate in those areas. Companies that have used recruitment algorithms have found that they amplify bias against women.

But there is an even more basic concern about algorithmic decision-making. Even in the absence of systematic class or racial bias, what if algorithms struggle to make even remotely accurate predictions about the trajectories of individuals' lives? That concern gains new support in a recent paper published in the Proceedings of the National Academy of Sciences. The paper describes a challenge, organized by a group of sociologists at Princeton University, involving 160 research teams from universities across the country and hundreds of researchers in total, including one of the authors of this article. These teams were tasked with analyzing data from the Fragile Families and Child Wellbeing Study, an ongoing study that measures various life outcomes for thousands of families who gave birth to children in large American cities around 2000. It is one of the richest data sets available to researchers: It tracks thousands of families over time, and has been used in more than 750 scientific papers.

The task for the teams was simple. They were given access to almost all of this data and asked to predict several important life outcomes for a sample of families. Those outcomes included the childs grade point average, their grit (a commonly used measure of passion and perseverance), whether the household would be evicted, the material hardship of the household, and whether the parent would lose their job.

The teams could draw on almost 13,000 predictor variables for each family, covering areas such as education, employment, income, family relationships, environmental factors, and child health and development. The researchers were also given access to the outcomes for half of the sample, and they could use this data to hone advanced machine-learning algorithms to predict each of the outcomes for the other half of the sample, which the organizers withheld. At the end of the challenge, the organizers scored the 160 submissions based on how well the algorithms predicted what actually happened in these peoples lives.

The results were disappointing. Even the best performing prediction models were only marginally better than random guesses. The models were rarely able to predict a students GPA, for example, and they were even worse at predicting whether a family would get evicted, experience unemployment, or face material hardship. And the models gave almost no insight into how resilient a child would become.

In other words, even having access to incredibly detailed data and modern machine learning methods designed for prediction did not enable the researchers to make accurate forecasts. The results of the Fragile Families Challenge, the authors conclude, with notable understatement, raise questions about the absolute level of predictive performance that is possible for some life outcomes, even with a rich data set.

Of course, machine learning systems may be much more accurate in other domains; this paper studied the predictability of life outcomes in only one setting. But the failure to make accurate predictions cannot be blamed on the failings of any particular analyst or method. Hundreds of researchers attempted the challenge, using a wide range of statistical techniques, and they all failed.

These findings suggest that we should doubt that big data can ever perfectly predict human behavior and that policymakers working in criminal justice policy and child-protective services should be especially cautious. Even with detailed data and sophisticated prediction techniques, there may be fundamental limitations on researchers' ability to make accurate predictions. Human behavior is inherently unpredictable, social systems are complex, and the actions of individuals often defy expectations.

And yet disappointing as this may be for technocrats and data scientists, it also suggests something reassuring about human potential. If life outcomes are not firmly pre-determined if an algorithm, given a set of past data points, cannot predict a persons trajectory then the algorithms limitations ultimately reflect the richness of humanitys possibilities.

Bryan Schonfeld and Sam Winter-Levy are PhD candidates in politics at Princeton University.

Link:
Algorithms may never really figure us out thank goodness - The Boston Globe

Addressing AI anxiety

A common narrative around emerging technologies like AI, machine learning, and robotic process automation is the anxiety and fear that theyll replace humans. In South Africa, with an unemployment rate of over 30%, these concerns are valid.

But if we dig deep into what we can do with AI, we learn it will elevate the work that humans do, making it more valuable than ever.

Sage research found that most senior financial decision-makers (90%) are comfortable with automation performing more of their day-to-day accounting tasks in the future, and 40% believe that AI and machine learning (ML) will improve forecasting and financial planning.

Whats more, two-thirds of respondents expect emerging technology to audit results continuously and to automate period-end reporting and corporate audits, reducing time to close in the process.

The key to realising these benefits is to secure buy-in from the entire organisation. With 87% of CFOs now playing a hands-on role in digital transformation, their perspective on technology is key to creating a digitally receptive team culture. And their leadership is vital in ensuring their organisations maximise their technology investments. Until employees make the same mindset shift as CFOs have, theyll need to be guided and reassured about the businesss automation strategy and the potential for upskilling.

Six benefits of AI in laymans terms

Speaking during an exclusive virtual event to announce the results of the CFO 3.0 research, as well as the launch of Sage Intacct in South Africa, Aaron Harris, CTO for the Sage, said one reason for the misperception about AIs impact on business and labour is that SaaS companies too often speak in technical jargon.

We talk about AI and machine learning as if theyre these magical capabilities, but we dont actually explain what they do and what problems they solve. We dont put it into terms that matter for business leaders and labour. We dont do a good job as an industry, explaining that machine learning isnt an outcome we should be looking to achieve its the technology that enables business outcomes, like efficiency gains and smarter predictive analytics.

For Harris, AI has remarkable benefits in six key areas:

Digital culture champions

Evolving from a traditional management style that relied on intuition, to a more contemporary one based on data-driven evidence, can be a culturally disruptive process. Interestingly, driving a cultural change wasnt a concern for most South African CFOs, with 73% saying their organisations are ready for more automation.

In fact, AI holds no fear for senior financial decision-makers: over two-thirds are not at all concerned about it, and only one in 10 believe that it will take away jobs.

So, how can businesses reimagine the work of humans when software bots are taking care of all the repetitive work?

How can we leverage the unique skills of humans, like collaboration, contextual understanding, and empathy?

The future world is a world of connections, says Harris. It will be about connecting humans in ways that allow them to work at a higher level. It will be about connecting businesses across their ecosystems so that they can implement digital business models to effectively and competitively operate in their markets. And it will be about creating connections across technology so that traditional, monolithic experiences are replaced with modern ones that reflect new ways of working and that are tailored to how individuals and humans will be most effective in this world.

New world of work

We can envision this world across three areas:

Sharing knowledge and timelines on strategic developments and explaining the significance of these changes will help CFOs to alleviate the fear of the unknown.

Technology may be the enabler driving this change, but how it transforms a business lies with those who are bold enough to take the lead. DM

See the article here:
Six notable benefits of AI in finance, and what they mean for humans - Daily Maverick

Electronic document management systems and third-party credit rating systems enable banks to process loan applications faster and other consumer inquiries with fewer efforts from humans.

Fremont, CA: The banking sector has seen a major transformation over the past years. Traditional financial processes are now replaced by digitalized systems. Although the banking sector has evolved, there are still some improvements required across the board. By concurrently processing various algorithms to determine patterns in consumer behaviors and obtaining data from documents to answer questions will reduce humans' inputs to complete different banking transactions.

Here are five uses of AI in the lending sector:

Digital Footprint Analysis Using AI

Artificial Intelligence (AI) gathers information about an applicant's digital behavior like the profiles of people they interact with more on social media, variable employment information, spending habits, and major organizations they belong to. This information can supplement and replace a credit score as the final factor in deciding whether to extend credit to an applicant.

Machine Learning Provides Credit Insights

The algorithms implemented in machine learning can examine non-numerical factors in evaluating an applicant's creditworthiness like their consumer behavior in other industries and social media activity. These latest credit scoring tools provide better insights into applicants' willingness to pay their debt, resulting in the extension of credit to deserving applicants.

Enhancing Risk-Adjustment Margins with AI

Implementing AI can accurately find the appropriate interest rate to charge on a consumer loan. Banks can effectively take advantage of their assets to optimize profits with the help of AI software in extending credit to deserving borrowers with an appropriate interest rate that will enable them to continue making payments in time on their loans.

AI and Risk Reduction

The utilization of AI in a bank's loan system decreases the chances of human error in processing a loan application or ignore the vital factors in determining if a borrower will default on a loan. AI also plays a crucial part in the bank's loan management system to distinguish patterns of behavior that shows if a customer may be close to declaring bankruptcy or defaulting on the debt. It will help banks save costly losses and retain credit availability for deserving borrowers who will become or continue to be economic participants by minimizing those risks.

Missed Opportunities from Relying on Credit Scores

Missed opportunities developed by banks' dependence on credit scores to access an applicant's creditworthiness limits consumers' economic activities and reduce the bank's profit potential.

See also:Top Artificial Intelligence Solution Companies

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How AI is Transforming the Digital Lending Landscape? - Banking CIO Outlook

The job of harvesting cauliflowers could one day be in the mechanical hands of robots thanks to a collaboration between scientists and the French canned vegetable producer Bonduelle.

Fieldwork Robotics, the team behind the worlds first raspberry-picking robot, is designing a machine in a three-year collaboration launched on Monday.

An early prototype already exists, developed by Fieldworks co-founder Dr Martin Stoelen, lecturer in robotics at the University of Plymouth and associate professor at the Western Norway University of Applied Science. It has a gripper and a cutter that can neatly slice off a cauliflower head.

It works in a lab environment, where we put a lot of cauliflower heads in a row, said Rui Andrs, Fieldworks chief executive. The robot is guided by sensors and 3D cameras and uses machine learning, a form of artificial intelligence.

A current favourite of foodies who have rescued it from its traditional insipid cheese sauce and are using it as an alternative to rice and pizza bases, but the vegetables do pose a challenge for robot designers.

Supermarkets and most consumers want white cauliflower, Andrs said. Cauliflower turns yellow in the sun, so self-blanching varieties have been developed with leaves that curl up over the head to shield it from sunlight. However, this makes it much harder for a robot to determine the ripeness.

Bonduelle will provide the necessary know-how about the vegetables and harvesting conditions, as well as access to fields.

Fieldwork Robotics, a spinout from the University of Plymouth, will collect data and feed it into computer algorithms that will power the robotic arms. Field trials are expected to start in early 2022.

Stoelen originally developed the robot system in a project funded by the European Regional Development Fund and Cornwall council. Fieldwork envisages a modular robot system that can be adapted for different fruit and vegetables (it has also been tested on tomatoes).

Britains departure from the EU has led to a decline in the number of seasonal workers coming to the UK to pick fruit and vegetables, and some growers looking to plug labour shortages have already expressed interest in the raspberry-picking robot.

Fieldwork raised 298,000 in January from its backers to scale up the technology, and has also been supported by a 547,250 Innovate UK grant. It is looking to raise a further 500,000 from existing and new investors.

Fieldworks has worked with Germanys Bosch to improve the software and design of the robot arms. The robot is about to get two more arms, and should be able to pick a raspberry in 2.5 seconds next year (currently 2.8 seconds) while humans take two seconds to pick a raspberry on average. However, the robot will be able to work right through the night.

Planned field trials in the spring could not go ahead because of the Covid-19 pandemic, but the robot will be back for testing in greenhouses next month. Fieldwork hopes to have a raspberry-picking prototype ready for manufacture by next summer.

See the article here:
Robots gear up to march to the fields and harvest cauliflowers - The Guardian