Understanding failures and attacks can help us build safer AI applications.

Modern machine learning (ML) has become an important tool in a very short time. We're using ML models across our organisations, either rolling our own in R and Python, using tools like TensorFlow to learn and explore our data, or building on cloud- and container-hosted services like Azure's Cognitive Services. It's a technology that helps predict maintenance schedules, spots fraud and damaged parts, and parses our speech, responding in a flexible way.

SEE:Prescriptive analytics: An insider's guide (free PDF)(TechRepublic)

The models that drive our ML applications are incredibly complex, training neural networks on large data sets. But there's a big problem: they're hard to explain or understand. Why does a model parse a red blob with white text as a stop sign and not a soft drink advert? It's that complexity which hides the underlying risks that are baked into our models, and the possible attacks that can severely disrupt the business processes and services we're building using those very models.

It's easy to imagine an attack on a self-driving car that could make it ignore stop signs, simply by changing a few details on the sign, or a facial recognition system that would detect a pixelated bandanna as Brad Pitt. These adversarial attacks take advantage of the ML models, guiding them to respond in a way that's not how they're intended to operate, distorting the input data by changing the physical inputs.

Microsoft is thinking a lot about how to protect machine learning systems. They're key to its future -- from tools being built into Office, to its Azure cloud-scale services, and managing its own and your networks, even delivering security services through ML-powered tools like Azure Sentinel. With so much investment riding on its machine-learning services, it's no wonder that many of Microsoft's presentations at the RSA security conference focused on understanding the security issues with ML and on how to protect machine-learning systems.

Attacks on machine-learning systems need access to the models used, so you need to keep your models private. That goes for small models that might be helping run your production lines as much as the massive models that drive the likes of Google, Bing and Facebook. If I get access to your model, I can work out how to affect it, either looking for the right data to feed it that will poison the results, or finding a way past the model to get the results I want.

Much of this work has been published in a paper in conjunction with the Berkman Klein Center, on failure modes in machine learning. As the paper points out, a lot of work has been done in finding ways to attack machine learning, but not much on how to defend it. We need to build a credible set of defences around machine learning's neural networks, in much the same way as we protect our physical and virtual network infrastructures.

Attacks on ML systems are failures of the underlying models. They are responding in unexpected, and possibly detrimental ways. We need to understand what the failure modes of machine-learning systems are, and then understand how we can respond to those failures. The paper talks about two failure modes: intentional failures, where an attacker deliberately subverts a system, and unintentional failures, where there's an unsafe element in the ML model being used that appears correct but delivers bad outcomes.

By understanding the failure modes we can build threat models and apply them to our ML-based applications and services, and then respond to those threats and defend our new applications.

The paper suggests 11 different attack classifications, many of which get around our standard defence models. It's possible to compromise a machine-learning system without needing access to the underlying software and hardware, so standard authorisation techniques can't protect ML-based systems and we need to consider alternative approaches.

What are these attacks? The first, perturbation attacks, modify queries to change the response to one the attackers desire. That's matched by poisoning attacks, which achieve the same result by contaminating the training data. Machine-learning models often include important intellectual property, and some attacks like model inversion aim to extract that data. Similarly, a membership inference attack will try to determine whether specific data was in the initial training set. Closely related is the concept of model stealing, using queries to extract the model.

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Other attacks include reprogramming the system around the ML model, so that either results or inputs are changed. Closely related are adversarial attacks that change physical objects, adding duct tape to signs to confuse navigation or using specially printed bandanas to disrupt facial-recognition systems. Some attacks depend on the provider: a malicious provider can extract training data from customer systems. They can add backdoors to systems, or compromise models as they're downloaded.

While many of these attacks are new and targeted specifically at machine-learning systems, they are still computer systems and applications, and are vulnerable to existing exploits and techniques, allowing attackers to use familiar approaches to disrupt ML applications.

It's a long list of attack types, but understanding what's possible allows us to think about the threats our applications face. More importantly they provide an opportunity to think about defences and how we protect machine-learning systems: building better, more secure training sets, locking down ML platforms, and controlling access to inputs and outputs, working with trusted applications and services.

Attacks are not the only risk: we must be aware of unintended failures -- problems that come from the algorithms we use or from how we've designed and tested our ML systems. We need to understand how reinforcement learning systems behave, how systems respond in different environments, if there are natural adversarial effects, or how changing inputs can change results.

If we're to defend machine-learning applications, we need to ensure that they have been tested as fully as possible, in as many conditions as possible. The apocryphal stories of early machine-learning systems that identified trees instead of tanks, because all the training images were of tanks under trees, are a sign that these aren't new problems, and that we need to be careful about how we train, test, and deploy machine learning. We can only defend against intentional attacks if we know that we've protected ourselves and our systems from mistakes we've made. The old adage "test, test, and test again" is key to building secure and safe machine learning -- even when we're using pre-built models and service APIs.

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Microsoft: This is how to protect your machine-learning applications - TechRepublic

Advances in computer power, machine-learning and predictive algorithms are creating paradigm shifts in many industries. For example, when analgorithm outperformed six radiologistsin reading mammograms and accurately diagnosing breast cancer, this raised questions around the role of machine-learning in medicine and whether it will replace, or enhance, the work being done by doctors.

Similarly, when Googles AI software AlphaGo beat the worlds top Go master in what is described as humankinds most complicated board game, The New York Timesdeclared it isnt looking good for humanity when an algorithm can outperform a human in a highly complex task.

Both these examples point to narrow uses of artificial intelligence, specific types of machine-learning that are hugely effective. The medical example illustrates supervised learning, where a computer is programmed to solve a particular problem by looking for patterns. It is given labelled data sets, in this case X-rays with the diagnosis of presence or absence of breast cancer. When given a new X-ray, the computer applies an algorithm based on what it has learnt from all the previous X-rays to make a diagnosis. Unsupervised learning is a sort of self-optimisation where a computer has a set of rules, such as how to play Go, and through playing millions of games learns how to apply these rules and improve.

What is machine-learning?

Machine-learning is a phenomenal tool. To fully harness its potential it is essential to understand what machine-learning is (and isnt) and to demystify some of the hype and the fear around what it can and cant be used for. We have anthropomorphised computers; we speak about them in terms of intelligence and learning. But in essence, a machine computes it does not learn. Its algorithms are designed to mimic learning. In essence, these algorithms minimise the errors of a complicated function that maps inputs to outcomes and we interpret that as solving a problem, but the machine doesnt know what problem it is solving or that it is playing a game. The intelligence rests with the humans who design the algorithms and configure them for specific tasks.

Now, more than ever, we need intelligent and well-educated people who can apply these techniques in the correct context and interpret the results. When an algorithm fails, the consequences can be catastrophic. An obvious example is a fatal accident caused by aself-driving car. We need to build in fault tolerance. Data integrity is also an important issue what we put in is going to affect what we get out. Education is critical in making sure we get these elements right. And, of course, there are broader ethical issues to consider surrounding data collection, such as what data can be used, where it is sourced, and whether different data sets can be combined.

Machine-learning is particularly valuable in the financial sector. Many applications are already in use in banking, insurance and asset management. Financial institutions use pattern recognition successfully for fraud detection. It is also valuable for looking at trends in data sets and finding patterns that humans may not be able to identify directly, for example in profiling people who apply for credit. There are even robo-advisory applications for individual asset allocation. In financial modelling, machine-learning can be applied to pricing, calibration and hedging.

For example, valuing derivatives contracts depends on many complex factors and variables such as interest rates, exchange rates, equity values all of which fluctuate all the time. Financial mathematicians use models for this, but they are complicated and not easy to solve in a closed form. We may be able to build and apply a model to one contract, but banks have hundreds of contracts, and risk management and regulatory frameworks need to be updated all the time. Machine-learning, specifically deep learning and neural nets, provides a powerful shortcut. We can use classical numerical methods to produce financial models and then use them as labelled data sets as in the X-ray example. An algorithm can take this input to generate the output for multiple contracts.

Industries and organisations that are pulling ahead are figuring out where to replace standard methods and complex, time-consuming computations with machine-learning. They are also using it for more complex modelling approaches, adding further variables that cannot usually be factored into standard methodologies. The most obvious benefit is that it is faster machines can compute millions of times faster than humans. These techniques also have the potential to be far more accurate and allow us to make better-informed decisions.

But the human element is critical. The accuracy of potentially life-changing outcomes will depend on how we identify where we use these techniques, how we build the algorithms, how we choose and manage data and, finally, in how we interpret and act upon the results.

Prof McWalter is an applied mathematician who lectures computational finance at UCTs African Institute of Financial Markets and Risk Management. Prof Kienitz lectures at the University of Wuppertal and is an adjunct associate professor at UCT. His research interests include numerical methods in finance and machine-learning applied to financial problems and derivative instruments.

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Machine-learning is a boon, but it still needs a human hand - Business Day

Global Machine Learning as a Service (MLaaS) Marketwas valued about US$ XX Bn in 2019 and is expected to grow at a CAGR of 41.7% over the forecast period, to reach US$ 11.3 Bn in 2027.

The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

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Market Definition:

Machine learning as a service (MLaaS) is an array of services that offer ML tools as part of cloud computing services. MLaaS helps clients profit from machine learning without the cognate cost, time and risk of establishing an in-house internal machine learning team.The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

Machine Learning Service Providers:

Global Machine Learning as a Service (MLaaS) Market

Market Dynamics:

The scope of the report includes a detailed study of global and regional markets for Global Machine Learning as a Service (MLaaS) Market with the analysis given with variations in the growth of the industry in each regions. Large and SMEs are focusing on customer experience management to keep a complete and robust relationship with their customers by using customer data. So, ML needs to be integrated into enterprise applications to control and make optimal use of this data. Retail enterprises are shifting their focus to customer buying patterns with the rising number of e-commerce websites and the digital revolution in the retail industry. This drives the need to track and manage the inventory movement of items, which can be done using MLaaS. The use of MLaaS by retail enterprises for inventory optimization and behavioral tracking is expected to have a positive impact on global market growth.Apart from this, the growing trend of digitization is driving the growth of the MLaaS market globally. Growth in adoption of cloud-based platforms is expected to positively impact the growth of the MLaaS market. However, a lack of qualified and skilled persons is believed to be the one of the challenges before the growth of the MLaaS market. Furthermore, increasing concern toward data privacy is anticipated to restrain the development of the global market.

Market Segmentation:

The report will provide an accurate prediction of the contribution of the various segments to the growth of the Machine Learning as a Service (MLaaS) Market size. Based on organization size, SMEs segment is expected to account for the largest XX% market share by 2027. SMEs businesses are also projected to adopt machine learning service. With the help of predictive analytics ML, algorithms not only give real-time data but also predict the future. Machine learning solutions are used by SME businesses for fine-tuning their supply chain by predicting the demand for a product and by suggesting the timing and quantity of supplies vital for satisfying the customers expectations.

Regional Analysis:

The report offers a brief analysis of the major regions in the MLaaS market, namely, Asia-Pacific, Europe, North America, South America, and the Middle East & Africa.North America play an important role in MLaaS market, with a market size of US$ XX Mn in 2019 and will be US$ XX Mn in 2027, with a CAGR of XX% followed by Europe. Most of the machine learning as service market companies are based in the U.S and are contributing significantly in the growth of the market. The Asia-Pacific has been growing with the highest growth rate because of rising investment, favorable government policies and growing awareness. In 2017, Google launched the Google Neural Machine Translation for 9 Indian languages which use ML and artificial neural network to upsurges the fluency as well as accuracy in their Google Translate.

Recent Development:

The MMR research study includes the profiles of leading companies operating in the Global Machine Learning as a Service (MLaas) Market. Companies in the global market are more focused on enhancing their product and service helps through various strategic approaches. The ML providers are competing by launching new product categories, with advanced subscription-based platforms. The companies have adopted the strategy of version up gradations, mergers and acquisitions, agreements, partnerships, and strategic collaborations with regional and global players to achieve high growth in the MLaaS market.

Such as, in April 2019, Microsoft developed a platform that uses machine teaching to help deep strengthening learning algorithms tackle real-world problems. Microsoft scientists and product inventors have pioneered a complementary approach called ML. This relies on people know how to break a problem into easier tasks and give ML models important clues about how to find a solution earlier.

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The objective of the report is to present a comprehensive analysis of the Global Machine Learning as a Service (MLaaS) Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language. The report covers all the aspects of the industry with a dedicated study of key players that includes market leaders, followers and new entrants by region. PORTER, SVOR, PESTEL analysis with the potential impact of micro-economic factors by region on the market has been presented in the report. External as well as internal factors that are supposed to affect the business positively or negatively have been analyzed, which will give a clear futuristic view of the industry to the decision-makers.

The report also helps in understanding Global Machine Learning as a Service (MLaaS) Market dynamics, structure by analyzing the market segments and projects the Global Machine Learning as a Service (MLaaS) Market size. Clear representation of competitive analysis of key players by Application, price, financial position, Product portfolio, growth strategies, and regional presence in the Global Machine Learning as a Service (MLaaS) Market make the report investors guide.Scope of the Global Machine Learning as a Service (MLaaS) Market

Global Machine Learning as a Service (MLaaS) Market, By Component

Software ServicesGlobal Machine Learning as a Service (MLaaS) Market, By Organization Size

Large Enterprises SMEsGlobal Machine Learning as a Service (MLaaS) Market, By End-Use Industry

Aerospace & Defense IT & Telecom Energy & Utilities Public sector Manufacturing BFSI Healthcare Retail OthersGlobal Machine Learning as a Service (MLaaS) Market, By Application

Marketing & Advertising Fraud Detection & Risk Management Predictive analytics Augmented & Virtual reality Natural Language processing Computer vision Security & surveillance OthersGlobal Machine Learning as a Service (MLaaS) Market, By Region

Asia Pacific North America Europe Latin America Middle East AfricaKey players operating in Global Machine Learning as a Service (MLaaS) Market

Ersatz Labs, Inc. BigML Yottamine Analytics Hewlett Packard Amazon Web Services IBM Microsoft Sift Science, Inc. Google AT&T Fuzzy.ai SAS Institute Inc. FICO Predictron Labs Ltd.

MAJOR TOC OF THE REPORT

Chapter One: Machine Learning as a Service Market Overview

Chapter Two: Manufacturers Profiles

Chapter Three: Global Machine Learning as a Service Market Competition, by Players

Chapter Four: Global Machine Learning as a Service Market Size by Regions

Chapter Five: North America Machine Learning as a Service Revenue by Countries

Chapter Six: Europe Machine Learning as a Service Revenue by Countries

Chapter Seven: Asia-Pacific Machine Learning as a Service Revenue by Countries

Chapter Eight: South America Machine Learning as a Service Revenue by Countries

Chapter Nine: Middle East and Africa Revenue Machine Learning as a Service by Countries

Chapter Ten: Global Machine Learning as a Service Market Segment by Type

Chapter Eleven: Global Machine Learning as a Service Market Segment by Application

Chapter Twelve: Global Machine Learning as a Service Market Size Forecast (2019-2026)

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Global Machine Learning As A Service (Mlaas) Market : Industry Analysis And Forecast (2020-2027) - MR Invasion

PRINCETON, N.J., April 29, 2020 /PRNewswire/ --Life sciences consulting firm Beghou Consulting recently hired industry veteran Janardhan Vellore to strengthen the firm's advanced analytics capabilities and technology solutions. Vellore will join as a vice president.

"Janardhan is an standout leader in the life sciences industry, especially in his innovative use of advanced analytics to get in front of emerging commercial challenges," said Beth Beghou, founder and managing director of Beghou Consulting. "As a result, he has become a trusted adviser to life sciences companies of all sizes. He will supplement our already strong advanced analytics team and play a key role in our growth efforts."

Vellore brings deep experience in end-to-end capabilities that shape and inform commercial strategy, including managed care and access, patient centricity, and marketing, digital and technology solutions. In addition, he'll bolster Beghou's offerings related to all aspects of commercial operations including forecasting, sales force design, segmentation, targeting and incentive compensation with particular expertise in launching new products.

Vellore previously held leadership roles at Bayer and Novartis Pharmaceuticals, where he led commercial analytics, market research and management science teams. He also served in a leadership role at Analytical Wizards, where he spearheaded growth of its advanced analytics practice area and commercialized cloud-based, big data platforms for the life sciences industry.

"Beghou consistently delivers premium value and top-notch results through subject matter expertise and seamless collaboration. Its superior client service and high-quality insights into commercial operations serve as key differentiators among its peers," said Vellore. "My experiences in-house at life sciences companies and working as a consultant have given me a unique perspective of a life sciences company's commercial challenges and opportunities. I'm excited to draw on those experiences to help more companies address their pressing commercial issues and drive greater success in the rapidly evolving marketplace."

Vellore earned an MBA from The Wharton School of the University of Pennsylvania, a master's degree in biomedical engineering from The University of Akron and a bachelor's degree from the Indian Institute of Technology, Madras. He is based in Princeton, New Jersey.

About Beghou ConsultingFounded in 1993, Beghou Consulting specializes in helping life sciences companies especially emerging pharma companies establish and manage commercial operations to better market and sell therapies. Deploying advanced analytics and proprietary technology, Beghou consultants have provided strategic counsel to the top pharmaceutical companies in the world, supporting some since infancy. Headquartered in Evanston, Ill., the firm has six offices and employs more than 150 professionals around the world. To learn more, visit http://www.beghouconsulting.com or follow us on Facebook and LinkedIn.

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Beghou Consulting hires senior executive to expand advanced analytics and machine learning capabilities - The Trentonian

Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.

The Global Machine learning Market Report provides an extensive assessment of the global Machine learning market development, revenue, and profitability of the market. It also evaluates the scope, attractiveness, economy database, potential, and trends in the global Machine learning market.

The report mainly intends to assist market players, Machine learning business owners, researchers, students, and stakeholders with comprehensive market intelligence. The historic and current status of the global Machine learning market is deeply elucidated in the report.

The report offers authentic and reliable projections up to 2025 and predicts potential significant incidents to occur in the market in the near future. The report also describes changing market dynamics, contemporary trends, restraints, limitations, growth-boosting forces, technological advancements, uneven supply-demand proportions, unstable market structure, and uncertainties are analyzed in the market report as it could pose considerable impacts on the global Machine learning market structure and profitability.

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Rivalry scenario for the global Machine learning market, including business data of leading companies:

Google Inc., Microsoft, IBM Watson, Amazon, Baidu, Intel, Facebook, Apple Inc., and Uber.

The global Machine learning industry environment is heavily emphasized in the report as it holds the potential to impact the Machine learning market growth in a positive or negative manner.

The industry environment incorporates provincial trade regulations, international trade disputes, market entry barriers, as well as social, political, financial, and atmospheric circumstances, which could affect market growth momentum. The potential and current market opportunities, challenges, risks, obstacles, uncertainties, and threats are also highlighted in the report.

The report further elaborates on the key facets of the global Machine learning market which includes, competition, leading competitors, industry environment, and crucial segments in the market. An in-depth analysis of each facet has been exhaustively examined in the report to offer clients an inclusive conception of the global Machine learning market.

The report also provides thorough market analysis by investigating the market through adept analytical tools such as SWOT, Porter's Five Forces analysis that sheds light on the market threats, weaknesses, strengths, and various bargaining powers.

Expansive survey of Global Machine learning Market 2020

Insights into Machine learning market segments:

The global Machine learning market competition is also highlighted in the report with precise assessments based on the leading players in the global Machine learning market. The report analyses the manufacturing processes, production technologies, volume, plants, locations, effective production techniques, value chain, raw material, concentration rate, distribution networks, and global appearance.

Their strategic moves such as mergers, venture, amalgamations, partnerships, product launches, and brand promotions are also evaluated in the report.

Moreover, it offers profound analysis of efforts taken by leading market players to push their sales activities and capture maximum buyers. It explores their product research, innovations, and, technology adoptions, developments.

Their financial assessments are also illuminated in the global Machine learning market report with accurate evaluations of their revenue, gross margin, sales volume, production cost, cost structure, product prices, capital investments, market share, growth rates, and CAGR.

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Current research: Global Machine Learning Market is predicted to grow with demand and future opportunities - WhaTech Technology and Markets News

ML and its services are only going to extend their influence and push the boundaries to new realms of the technology revolution. However, deploying ML comes with great responsibility. Though efforts are being made to shed its black box reputation, it is crucial to establish trust in both in-house teams and stakeholders for a fairer deployment. Companies have started to take machine learning model management more seriously now. Recently, a machine learning company Comet.ml, based out of Seattle and founded in 2017, announced that they are making a $4.5 million investment to bring state-of-the-art meta-learning capabilities to the market.

The tools developed by Comet.ml enable data scientists to track, compare, monitor, and optimise model development. Their announcement of an additional $4.5 million investment from existing investors Trilogy Equity Partners and Two Sigma Ventures is aimed at boosting their plans to domesticate the use of machine learning model management techniques to more customers.

Since their product launch in 2018, Comet.ml has partnered with top companies like Google, General Electric, Boeing and Uber. This elite list of customers use comet.al services, which have enterprise-level toolkits, and are used to train models across multiple industries spanning autonomous vehicles, financial services, technology, bioinformatics, satellite imagery, fundamental physics research, and more.

Talking about this new announcement, one of the investors, Yuval Neeman of Trilogy Equity Partners, reminded that the professionals from the best companies in the world choose Comet and that the company is well-positioned to become the de-facto Machine Learning development platform.

This platform, says Neeman, allows customers to build ML models that bring significant business value.

According to a report presented by researchers at Google, there are several ML-specific risk factors to account for in system design, such as:

Debugging all these issues require round the clock monitoring of the models pipeline. For a company that implements ML solutions, it is challenging to manage in-house model mishaps.

If we take the example of Comet again, its platform provides a central place for the team to track their ML experiments and models, so that they can compare and share experiments, debug and take decisive actions on underperforming models with great ease.

Predictive early stopping is a meta-learning functionality not seen in any other experimentation platforms, and this can be achieved only by building on top of millions of public models. And this is where Comets enterprise products come in handy. The freedom of experimentation that these meta learning-based platforms offer is what any organisation would look up to. Almost all ML-based companies would love to have such tools in their arsenal.

Talking about saving the resources, Comet.ml in their press release, had stated that their platform led to the improvement of model training time by 30% irrespective of the underlying infrastructure, and stopped underperforming models automatically, which reduces cost and carbon footprint by 30%.

Irrespective of the underlying infrastructure, it stops underperforming models automatically, which reduces cost and carbon footprint by 30%.

The enterprise offering also includes Comets flagship visualisation engine, which allows users to visualise, explain, and debug model performance and predictions, and a state-of-the-art parameter optimisation engine.

When building any machine learning pipeline, data preparation requires operations like scraping, sampling, joining, and plenty of other approaches. These operations usually accumulate haphazardly and result in what the software engineers would like to call a pipeline jungle.

Now, add in the challenge of forgotten experimental code in the code archives. Things only get worse. The presence of such stale code can malfunction, and an algorithm that runs this malfunctioning code can crash stock markets and self-driving cars. The risks are just too high.

So far, we have seen the use of ML for data-driven solutions. Now the market is ripe for solutions that help those who have already deployed machine learning. It is only a matter of time before we see more companies setting up their meta-learning shops or partner with third-party vendors.

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Is Machine Learning Model Management The Next Big Thing In 2020? - Analytics India Magazine

NEW DELHI: iPhone SE, the first iPhone that Apple has launched in 2020, is also the first iPhone in the companys lineup to use "Single Image Monocular Depth Estimation, as per a blog post by Halide, a popular camera app.This means that the latest generation of iPhone SE is the first iPhone that can generate a portrait effect using nothing but a single, 2D image, claims the app. Readers must note that even though the iPhone XR also offers a single rear camera, it does obtain depth information through hardware. It tapped into the sensors focus pixels, which you can think of as tiny pairs of eyes designed to help with focus. The XR uses the very slight differences seen out of each eye to generate a very rough depth map, says the blog post.However, unlike the iPhone XR, the iPhone SE doesnt use focus pixels as it offers an older sensor same as iPhone 8 as claimed by iFixit that apparently doesnt have enough coverage. Therefore, the depth effect generated by the budget iPhone is said to be based completely on machine learning. Therefore, the iPhone SE is capable of capturing photos in Portrait Mode from both the back and front camera, claims Apple Insider, thanks to the powerful A13 Bionic chipset with a third-generation Neural Engine, which powers the much more expensive iPhone 11 lineup. Meanwhile, the iPhone SE scored a 6 out of 10 repairability score on iFixit ranking, in the overall teardown. Display and battery are said to be the same as the iPhone 8 are easily fixable which is a good thing as these are two more commonly replaced components of smartphones. However, the glass back is said to be fragile and impractical to replace.

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iPhone SE and the 'art' of machine learning - Gadgets Now

Monitors display a video showing facial recognition software in use at the headquarters of the artificial intelligence company Megvii, in Beijing, May 10, 2018. Beijing is putting billions of dollars behind facial recognition and other technologies to track and control its citizens.

Gilles Sabri | The New York Times

The world is facing its biggest health crisis in decades but one of the world's most promising technologies artificial intelligence (AI) isn't playing the major role some may have hoped for.

Renowned AI labs at the likes of DeepMind, OpenAI, Facebook AI Research, and Microsoft have remained relatively quiet as the coronavirus has spread around the world.

"It's fascinating how quiet it is," said Neil Lawrence, the former director of machine learning at Amazon Cambridge.

"This (pandemic) is showing what bulls--t most AI hype is. It's great and it will be useful one day but it's not surprising in a pandemic that we fall back on tried and tested techniques."

Those techniques include good, old-fashioned statistical techniques and mathematical models. The latter is used to create epidemiological models, which predict how a disease will spread through a population. Right now, these are far more useful than fields of AI like reinforcement learning and natural-language processing.

Of course, there are a few useful AI projects happening here and there.

In March, DeepMind announced that it hadused a machine-learning technique called "free modelling" to detail the structures of six proteins associated with SARS-CoV-2, the coronavirus that causes the Covid-19 disease.Elsewhere, Israeli start-up Aidoc is using AI imaging to flag abnormalities in the lungs and a U.K. start-up founded by Viagra co-inventor David Brown is using AI to look for Covid-19 drug treatments.

Verena Rieser, a computer science professor at Heriot-Watt University, pointed out that autonomous robots can be used to help disinfect hospitals and AI tutors can support parents with the burden of home schooling. She also said "AI companions" can help with self isolation, especially for the elderly.

"At the periphery you can imagine it doing some stuff with CCTV," said Lawrence, adding that cameras could be used to collect data on what percentage of people are wearing masks.

Separately, a facial recognition system built by U.K. firm SCC has also been adapted to spot coronavirus sufferers instead of terrorists.In Oxford, England, Exscientia is screening more than 15,000 drugs to see how effective they are as coronavirus treatments. The work is being done in partnership withDiamond Light Source, the U.K.'s national "synchotron."

But AI's role in this pandemic is likely to be more nuanced than some may have anticipated. AI isn't about to get us out of the woods any time soon.

"It's kind of indicating how hyped AI was," said Lawrence, who is now a professor of machine learning at the University of Cambridge. "The maturity of techniques is equivalent to the noughties internet."

AI researchers rely on vast amounts of nicely labeled data to train their algorithms, but right now there isn't enough reliable coronavirus data to do that.

"AI learns from large amounts of data which has been manually labeled a time consuming and expensive task," said Catherine Breslin, a machine learning consultant who used to work on Amazon Alexa.

"It also takes a lot of time to build, test and deploy AI in the real world. When the world changes, as it has done, the challenges with AI are going to be collecting enough data to learn from, and being able to build and deploy the technology quickly enough to have an impact."

Breslin agrees that AI technologies have a role to play. "However, they won't be a silver bullet," she said, adding that while they might not directly bring an end to the virus, they can make people's lives easier and more fun while they're in lockdown.

The AI community is thinking long and hard about how it can make itself more useful.

Last week, Facebook AI announced a number of partnerships with academics across the U.S.

Meanwhile, DeepMind's polymath leader Demis Hassabis is helping the Royal Society, the world's oldest independent scientific academy, on a new multidisciplinary project called DELVE (Data Evaluation and Learning for Viral Epidemics). Lawrence is also contributing.

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A.I. can't solve this: The coronavirus could be highlighting just how overhyped the industry is - CNBC

Perhaps the most important step we can all take to mitigate the spread of the coronavirus, also known as COVID-19, is to actively practice social distancing.

Why? Because the further away you are from another person, the less likely you'll contract or transmit COVID-19.

But when we go about our daily routines, especially when out on a grocery run or heading to the hospital, social distancing can be a challenging task to uphold.

And some of us just have God awful spatial awareness in general.

But how do we monitor and enforce social distancing when looking at a mass population? We resort to the wonders of artificial intelligence (AI), of course.

In a recent blog post, the company demonstrated a nifty social distancing detector that shows a feed of people walking along a street in the Oxford Town Center of the United Kingdom.

The tool encompasses every individual in the feed with a rectangle. When they're properly observing social distancing, that rectangle is green. But when they get too close to another person (less than 6 feet away), the rectangle turns red, accompanied by a line 'linking' the two people that are too close to one another.

On the right-hand side of the tool there's a 'Bird's-Eye View' that allows for monitoring on a bigger scale. Every person is represented by a dot. Working the same way as the rectangles, the dots are green when social distancing is properly adhered to. They turn red when people get too close.

More specifically, work settings like factory floors where physical space is abundant, thus making manual tracking extremely difficult.

According to Landing AI CEO and Founder Andrew Ng, the technology was developed in response to requests by their clients, which includes Foxconn, the main manufacturer of Apple's prized iPhones.

The company also says that this technology can be integrated into existing surveillance cameras. However, it's still exploring ways in which to alert people when they get too close to each other. One possible method is the use of an audible alarm that rings when individuals breach the minimum distance required with other people.

According to Reuters, Amazon already uses a similar machine-learning tool to monitor its employees in their warehouses. In the name of COVID-19 mitigation, companies around the world are grabbing whatever machine-learning AI tools they can get in order to surveil their employees. A lot of these tools tend to be cheap, off-the-shelf iterations that allow employers to watch their employees and listen to phone calls as well.

Landing AI insists that their tool is only for use in work settings, even including a little disclaimer that reads "The rise of computer vision has opened up important questions about privacy and individual rights; our current system does not recognize individuals, and we urge anyone using such a system to do so with transparency and only with informed consent."

Whether companies that make use of this tool adhere to that, we'll never really know.

But we definitely don't want Big Brother to be watching our every move.

Cover image sourced from New Straits Times / AFP.

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This AI tool uses machine learning to detect whether people are social distancing properly - Mashable SE Asia