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Archive for the ‘Machine Learning’ Category

How to Beat Analysts and the Stock Market with Machine Learning – Knowledge@Wharton

Posted: October 19, 2020 at 3:55 am


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Analyst expectations of firms earnings are on average biased upwards, and that bias varies over time and stocks, according to new research by experts at Wharton and elsewhere. They have developed a machine-learning model to generate a statistically optimal and unbiased benchmark for earnings expectations, which is detailed in a new paper titled, Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases. According to the paper, the model has the potential to deliver profitable trading strategies: to buy low and sell high. When analyst expectations are too pessimistic, investors should buy the stock. When analyst expectations are excessively optimistic, investors can sell their holdings or short stocks as price declines are forecasted.

[With the machine-learning model], we can predict how the prices of the stocks will behave based on whether or not the analyst forecast is too optimistic or too pessimistic, said Wharton finance professor Jules H. van Binsbergen, who is one of the papers authors. His co-authors are Xiao Han, a doctoral student at the University of Edinburgh Business School; and Alejandro Lopez-Lira, a finance professor at the BI Norwegian Business School.

The researchers found that the biases of analysts increase in the forecast horizon, or in the period when the earnings announcement date is not anytime soon. However, on average, analysts revise their expectations downwards as the date of the earnings announcement approaches. These revisions induce negative cross-sectional stock predictability, the researchers write, explaining that stocks with more optimistic expectations earn lower subsequent returns. At the same time, corporate managers have more information about their own firms than investors have, and can use that informational advantage by issuing fresh stock, Binsbergen and his co-authors note.

The Opportunity to Profit

Comparing analysts earnings expectations with the benchmarks provided by the machine-learning algorithm reveals the degree of analysts biases, and the window of opportunity it opens. Binsbergen explained how investors could profit from their machine-learning model. With our machine-learning model, we can measure the mistakes that the analysts are making by taking the difference between what theyre forecasting and what our machine-learning forecast estimates, he said.

We can measure the mistakes that the analysts are making by taking the difference between what theyre forecasting and what our machine-learning forecast estimates. Jules H. van Binsbergen

Using that arbitrage opportunity, investors could short-sell stocks for which analysts are overly optimistic, and book their profits when the prices come down to realistic levels as the earnings announcement date approaches, said Binsbergen. Similarly, they could buy stocks for which analysts are overly pessimistic, and sell them for a profit when their prices rise to levels that correspond with earnings that turn out to be higher than forecasted, he added.

Binsbergen identified two main findings of the latest research. One is how optimistic analysts are substantially over time. Sometimes the bias is higher, and sometimes it is lower. That holds for the aggregate, but also for individual stocks, he said. With our method, you can track over time the stocks for which analysts are too optimistic or too pessimistic. That said, there are more stocks for which analysts are optimistic than theyre pessimistic, he added.

The second finding of the study is that there is quite a lot of difference between stocks in how biased the analysts are, said Binsbergen. So, its not that were just making one aggregate statement, that on average for all stocks the analysts are too optimistic.

Capital-raising Window for Corporations

Corporations, too, could use the machine-learning algorithms measure for analysts biases. If you are a manager of a firm who is aware of those biases, then in fact you can benefit from that, said Binsbergen. If the price is high, you can issue stocks and raise money. Conversely, if analysts negative biases push down the price of a stock, they serve as a signal for the firm to avoid issuing fresh stock at that time.

When analysts biases lift or depress a stocks price, it implies that the markets seem to be buying the analysts forecasts and were not correcting them for over-optimism or over-pessimism yet, Binsbergen said. With the machine-learning model that he and his researchers have developed, you can have a profitable investment strategy, he added. That also means that the managers of the firms whose stock prices are overpriced can issue stocks. When the stock is underpriced they can either buy back stocks, or at least refrain from issuing stocks.

For their study, the researchers used information from firms balance sheets, macroeconomic variables, and analysts predictions. They constructed forecasts for annual earnings that are a year and two years ahead for annual earnings; similarly, they used forecasts that were one, two and three quarters ahead for quarterly earnings. With the benchmark expectation provided by their machine-learning algorithm, they then calculated the bias in expectations as the difference between the analysts forecasts and the machine-learning forecasts.

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How to Beat Analysts and the Stock Market with Machine Learning - Knowledge@Wharton

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October 19th, 2020 at 3:55 am

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AI and Machine Learning Can Help Fintechs if We Focus on Practical Implementation and Move Away from Overhyped Narratives, Researcher Says – Crowdfund…

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Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used by Fintech platform developers to make more intelligent or informed decisions regarding key processes. This may include using AI to identify potentially fraudulent transactions, determining the creditworthiness of a borrower applying for a loan, and many other use cases.

Research conducted by Accenture found that 87% of business owners in the United Kingdom claim that theyre struggling with finding the best ways to adopt AI or ML technologies. Three out of four or 75% of C-Suite executives responding to Accentures survey said they really need to effectively adopt AI solutions within 5 years, so that they dont lose business to competitors.

As reported by IT Pro Portal, theres currently a gap between what may be considered just hype and actual or practical implementation of AI technologies and platforms.

Less than 5% of firms have actually managed to effectively apply Ai, meanwhile, more than 80% are currently just exploring basic proof of concepts for applying AL or ML algorithms. Many firms are also not familiar or dont have the expertise to figure out how to best apply these technologies to specific business use cases.

Yann Stadnicki, an experienced technologist and research engineer, argues that these technologies can play a key role in streamlining business operations. For example, they can help Fintech firms with lowering their operational costs while boosting their overall efficiency. They can also make it easier for a companys CFO to do their job and become a key player when it comes to supporting the growth of their firm.

Stadnicki points out that a research study suggests that company executives werent struggling to adopt AI solutions due to budgetary constraints or limitations. He adds that the study shows there may be certain operational challenges when it comes to effectively integrating AI and ML technologies.

He also mentions:

The inability to set up a supportive organizational structure, the absence of foundational data capabilities, and the lack of employee adoption are barriers to harnessing AI and machine learning within an organization.

He adds:

For businesses to harness the benefits of AI and machine learning, there needs to be a move away from an overhyped theoretical narrative towards practical implementation.It is important to formulate a plan and integration strategy of how your business will use AI and ML, to both mitigate the risks of cybercrime and fraud, while embracing the opportunity of tangible business impact.

Fintech firms and organizations across the globe are now leveraging AI and ML technologies to improve their products and services. In a recent interview with Crowdfund Insider, Michael Rennie, a U.K.-based product manager for Mendix, a Siemens business and the global leader in enterprise low-code, explained how emerging tech can be used to enhance business processes.

He noted:

Prior to low-code, the application and use of cutting-edge technologies within the banking sector have been more academic than actual. But low-code now enables you to apply emerging technologies like AI in a practical way so that they actually make an impact. For example, you could pair a customer-focused banking application built with low-code with a machine learning (ML) engine to identify user behaviors. Then you could make more informed decisions about where to invest in customer experience and most benefit your business.

He added:

Its easy to see the value in this. The problem is that without the correct technology, its too difficult to integrate traditional customer-facing applications with new technology systems. Such integrations typically require millions of dollars in investment and years of work. By the time an organization finishes that intensive work, the market may have moved on. Low-code eliminates that problem, makes integration easy and your business more agile.

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AI and Machine Learning Can Help Fintechs if We Focus on Practical Implementation and Move Away from Overhyped Narratives, Researcher Says - Crowdfund...

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October 19th, 2020 at 3:55 am

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Proximity matters: Using machine learning and geospatial analytics to reduce COVID-19 exposure risk – Healthcare IT News

Posted: September 20, 2020 at 10:56 pm


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Since the earliest days of the COVID-19 pandemic, one of the biggest challenges for health systems has been to gain an understanding of the community spread of this virus and to determine how likely is it that a person walking through the doors of a facility is at a higher risk of being COVID-19 positive.

Without adequate access to testing data, health systems early-on were often forced to rely on individuals to answer questions such as whether they had traveled to certain high-risk regions. Even that unreliable method of assessing risk started becoming meaningless as local community spread took hold.

Parkland Health & Hospital System, the safety net health system for Dallas County, Texas, and PCCI, a Dallas-based non-profit with expertise in the practical applications of advanced data science and social determinants of health, had a better idea.

Community spread of an infectious disease is made possible through physical proximity and density of active carriers and non-infected individuals. Thus, to understand the risk of an individual contracting the disease (exposure risk), it was necessary to assess their proximity to confirmed COVID-19 cases based on their address and population density of those locations.

If an "exposure risk" index could be created, then Parkland could use it to minimize exposure for their patients and health workers and provide targeted educational outreach in highly vulnerable zip codes.

PCCIs data science and clinical team worked diligently in collaboration with the Parkland Informatics team to develop an innovative machine learning driven predictive model called Proximity Index. Proximity Index predicts for an individuals COVID-19 exposure risk, based on their proximity to test positive cases and the population density.

This model was put into action at Parkland through PCCIs cloud-based advanced analytics and machine learning platform called Isthmus. PCCIs machine learning engineering team generated geospatial analysis for the model and, with support from the Parkland IT team, integrated it with their electronic health record system.

Since April 22, Parklands population health team has utilized the Proximity Index for four key system-wide initiatives to triage more than 100,000 patient encounters and to assess needs, proactively:

In the future, PCCI is planning on offering Proximity Index to other organizations in the community schools, employers, etc., as well as to individuals to provide them with a data driven tool to help in decision making around reopening the economy and society in a safe, thoughtful manner.

Many teams across the Parkland family collaborated on this project, including the IT team led by Brett Moran, MD, Senior Vice President, Associate Chief Medical Officer and Chief Medical Information Officer at Parkland Health and Hospital System.

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Proximity matters: Using machine learning and geospatial analytics to reduce COVID-19 exposure risk - Healthcare IT News

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September 20th, 2020 at 10:56 pm

Posted in Machine Learning

PREDICTING THE OPTIMUM PATH – Port Strategy

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A joint venture has seen the implementation of machine learning at HHLAs Container Terminal Burchardkai to optimise import container yard positioning and reduce re-handling moves.

The elimination of costly re-handling moves of import containers has recently been the focus of a joint project between container terminal operator HHLA, its affi liate Hamburg Port Consulting (HPC) and INFORM the Artificial Intelligence (AI) systems supplier. Machine learning sits at the heart of the system.

Dwell time is the unit of time used to measure the period in which a container remains in a container terminal with this typically running from its arrival off a vessel until leaving the terminal via truck, rail or another vessel.

For import containers there is often no specific information available on the pick-up time when selecting a storage slot in the container stack. This can lead to an inefficient container storage location in the yard generating, in turn, the requirement for additional shuffle moves that require extra resources including maintenance and energy consumption.

To mitigate this operational inefficiency, the project partners - HHLA, HPC and INFORM - have recently run a pilot project at HHLAs Container Terminal Burchardkai (CTB) focused on machine learning technology with this applied in order to predict individual import container dwell times and thereby reduce costly re-handling/shuffle moves.

As a specialist in IT software integration and terminal operations, HPC employed the deep learning approach to identify hidden patterns from historical data of container moves at HHLA CTB. This was undertaken over a period of two years and with the acquired information processed into high quality data sets. Assessed by the Syncrotess Machine Learning Module from INFORM and validated by the HPC simulation tool, the results show a significant reduction of shuffle moves resulting in a reduced truck turn time.

PRODUCTIVE IMPLEMENTATION

Dr. Alexis Pangalos, Partner at HPC discussing the project highlights notes: It was a productive implementation of INFORMs Artificial Intelligence (AI) solution for the choice of container storage positions at CTB. The Machine Learning (ML) Module was trained with data from CTBs container handling operations and the outcome from this is a system tailor-made for HHLAs operations.

HPC together with INFORM have integrated the Syncrotess ML Module into the slot allocation algorithms already running within CTBs terminal control system, ITS.

PREDICTING DWELL TIME

INFORMs AI solution predicts the dwell time (i.e., the time period the container is expected to be stored in the yard) and the outbound mode of transport (e.g., rail, truck, vessel) both of which are crucial criteria for selecting an optimised container storage location within the yard. A location that avoids unnecessary re-handling.

Utilising machine learning and AI and integrating these technologies into existing IT infrastructure are the success factors for reaching the next level of optimisations, says Jens Hansen, Executive Board Member responsible for IT at HHLA. A detailed analysis, and a smooth interconnectivity between all different systems, enable the value of improved safety while reducing costs and greenhouse gas emissions, he underlines.

DETAILED DOMAIN KNOWLEDGE

Data availability and data processing are key elements when it comes to utilising AI technology, says Pangalos. It requires a detailed domain knowledge of terminal operations to unlock greater productivity of the terminal equipment and connected processes.

The implementation is based on a machine learning assessment INFORM undertook in 2018 whereby it set out to determine if they could improve optimisation and operational outcomes using INFORMs broader ML algorithms developed for use in other industries such as finance and aviation.

As of 2019, system results indicated a prediction accuracy of 26% for dwell time predictions and 33% for outbound mode of transport predictions.

Dr. Eva Savelsberg, Senior Vice President of INFORMs Logistic Division notes: AI and machine learning allows us to leverage data from our past performance to inform us about how best to approach our future operations our ML Module gives our Operations Research based algorithms the best footing for making complex decisions about what to do in the future.

INFORMs Machine Learning Module allows CTB to leverage insights generated from algorithms that continuously learn from historical data."

Further Information: Matthew Wittemeier m.wittemeier@inform-software.com

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PREDICTING THE OPTIMUM PATH - Port Strategy

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September 20th, 2020 at 10:56 pm

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What is ‘custom machine learning’ and why is it important for programmatic optimisation? – The Drum

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Wayne Blodwell, founder and chief exec of The Programmatic Advisory & The Programmatic University, battles through the buzzwords to explain why custom machine learning can help you unlock differentiation and regain a competitive edge.

Back in the day, simply having programmatic on plan was enough to give you a competitive advantage and no one asked any questions. But as programmatic has grown, and matured (84.5% of US digital display spend is due to be bought programmatically in 2020, the UK is on track for 92.5%), whats next to gain advantage in an increasingly competitive landscape?

Machine Learning

[noun]

The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

(Oxford Dictionary, 2020)

Youve probably head of machine learning as it exists in many Demand Side Platforms in the form of automated bidding. Automated bidding functionality does not require a manual CPM bid input nor any further bid adjustments instead, bids are automated and adjusted based on machine learning. Automated bids work from goal inputs, eg achieve a CPA of x or simply maximise conversions, and these inputs steer the machine learning to prioritise certain needs within the campaign. This tool is immensely helpful in taking the guesswork out of bids and the need for continual bid intervention.

These are what would be considered off-the-shelf algorithms, as all buyers within the DSP have access to the same tool. There is a heavy reliance on this automation for buying, with many even forgoing traditional optimisations for fear of disrupting the learnings and holding it back but how do we know this approach is truly maximising our results?

Well, we dont. What we do know is that this machine learning will be reasonably generic to suit the broad range of buyers that are activating in the platforms. And more often than not, the functionality is limited to a single success metric, provided with little context, which can isolate campaign KPIs away from their true overarching business objectives.

Custom machine learning

Instead of using out of the box solutions, possibly the same as your direct competitors, custom machine learning is the next logical step to unlock differentiation and regain an edge. Custom machine learning is simply machine learning that is tailored towards specific needs and events.

Off-the-self algorithms are owned by the DSPs; however, custom machine learning is owned by the buyer. The opportunity for application is growing, with leading DSPs opening their APIs and consoles to allow for custom logic to be built on top of existing infrastructure. Third party machine learning partners are also available, such as Scibids, MIQ & 59A, which will develop custom logic and add a layer onto the DSPs to act as a virtual trader, building out granular strategies and approaches.

With this ownership and customisation, buyers can factor in custom metrics such as viewability measurement and feed in their first party data to align their buying and success metrics with specific business goals.

This level of automation not only provides a competitive edge in terms of correctly valuing inventory and prioritisation, but the transparency of the process allows trust to rightfully be placed with automation.

Custom considerations

For custom machine learning to be effective, there are a handful of fundamental requirements which will help determine whether this approach is relevant for your campaigns. Its important to have conversations surrounding minimum event thresholds and campaign size with providers, to understand how much value you stand to gain from this path.

Furthermore, a custom approach will not fix a poor campaign. Custom machine learning is intended to take a well-structured and well-managed campaign and maximise its potential. Data needs to be inline for it to be adequately ingested and for real insight and benefit to be gained. Custom machine learning cannot simply be left to fend for itself; it may lighten the regular day to day load of a trader, but it needs to be maintained and closely monitored for maximum impact.

While custom machine learning brings numerous benefits to the table transparency, flexibility, goal alignment its not without upkeep and workflow disruption. Levels of operational commitment may differ depending on the vendors selected to facilitate this customisation and their functionality, but generally buyers must be willing to adapt to maximise the potential that custom machine learning holds.

Find out more on machine learning in a session The Programmatic University are hosting alongside Scibids on The Future Of Campaign Optimisation on 17 September. Sign up here.

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What is 'custom machine learning' and why is it important for programmatic optimisation? - The Drum

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September 20th, 2020 at 10:56 pm

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How Machine Learning is Set to Transform the Online Gaming Community – Techiexpert.com – TechiExpert.com

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We often equate machine learning to fictional scenarios such as those presented in films including the Terminator franchise and 2001: A Space Odyssey. While these are all entertaining stories, the fact of the matter is that this type of artificial intelligence is not nearly as threatening. On the contrary, it has helped to dramatically enhance the overall user experience (UX) and to streamline many online functions (such as common search results) that we take for granted. Machine learning is also making its presence known within the digital gaming community. Without becoming overly technical, what transformations can we expect to witness and how will these impact the experience of the average gaming enthusiast?

Although games such as Pong and Super Mario Bros. were entertaining for their time, they were also quite predictable. This is why so many users have uploaded speed runs onto websites such as YouTube. However, what if a game actually learned from your previous actions? It is obvious that the platform itself would be much more challenging. This concept is now becoming a reality.

Machine learning can also apply to numerous scenarios. It may be used to provide a greater sense of realism with interacting with a role-playing game. It could be employed to offer speech recognition and to recognise voice commands. Machine learning may also be implemented to create more realistic non-playable characters (NPCs).

Whether referring to fast-paced MMORPGs to traditional forms of entertainment including slot games offered by websites such as scandicasino.vip, there is no doubt that machine learning will soon make its presence known.

We can clearly see that the technical benefits associated with machine learning will certainly be leveraged by game developers. However, it is just as important to mention that this very same technology will have a pronounced impact upon the players themselves. This is largely due to how games can be personalised based around the needs of the player.

We are not only referring to common options such as the ability to modify avatars and skins in this case. Instead, games are evolving to the point that they will base their recommendations off of the behaviours of the players themselves. For example, a plot may change as a result of how a player interacts with other characters. The difficulty of a specific level may be automatically adjusted in accordance with the skill of the player. As machine learning and AI both have the ability to model extremely complex systems, the sheer attention to graphical detail within the games (such as character features and backgrounds) will also become vastly enhanced.

We can see that the future of gaming looks extremely bright thanks to the presence of machine learning. While such systems might appear to have little impact upon traditional platforms such as solitaire, there is no doubt that they will still be felt across numerous other genres. So, get ready for a truly amazing experience in the months and years to come!

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September 20th, 2020 at 10:56 pm

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Current and future regulatory landscape for AI and machine learning in the investment management sector – Lexology

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On Tuesday this week, Mark Lewis, senior consultant in IT, fintech and outsourcing at Macfarlanes, took part in an event hosted by The Investment Association covering some of the use cases, successes and challenges faced when implementing AI and machine learning (AIML) in the investment management industry.

Mark led the conversation on the current regulatory landscape for AIML and on the future direction of travel for the regulation of AIML in the investment management sector. He identified several challenges posed by the current regulatory framework, including those caused by the lack of a standard definition of AI generally and for regulatory purposes. This creates the risk of a fragmented regulatory landscape (an expression used recently by the World Federation of Exchanges in the context of lack of a standard taxonomy for fintech globally) as different regulators tend to use different definitions of AIML. This results in the risk of over- or under-regulating AIML and is thought to be inhibiting firms adopting new AI systems. While the UK Financial Conduct Authority (FCA) and the Bank of England seem to have settled, at least for now, on a working definition of AI as the use of a machine to perform tasks normally requiring human intelligence, and of ML as a subset of AI where a machine teaches itself to perform tasks without being explicitly programmed these working definitions are too generic to be of serious practical use in approaching regulation.

The current raft of legislation and other regulation that can apply to AI systems is uncertain, vast and complex, particularly within the scope of regulated financial services. Part of the challenge is that, for now, there is very little specific regulation directly applicable to AIML (exceptions include GDPR and, for algorithmic high-frequency trading, MiFID II). The lack of understanding of new AIML systems, combined with an uncertain and complex regulatory environment, also has an impact internally within businesses as they attempt to implement these systems. Those responsible for compliance are reluctant to engage where sufficient evidence is not available on how the systems will operate and how great the compliance burden will be. Improvements in explanations from technologists may go some way to assisting in this area. Overall, this means that regulated firms are concerned that their current systems and governance processes for technology, digitisation and related services deployments remain fit-for-purpose when extended to AIML. They are seeking reassurance from their regulators that this is the case. Firms are also looking for informal, discretionary regulatory advice on specific AIML concerns, such as required disclosures to customers about the use of chatbots.

Aside from the sheer volume of regulation that could apply to AIML development and deployment, there is complexity in the sources of regulation. For example, firms must also have regard to AIML ethics and ethical standards and policies. In this context, Mark noted that, this year, the FCA and The Alan Turing Institute launched a collaboration on transparency and explainability of AI in the UK financial services sector, which will lead to the publication of ethical standards and expectations for firms deploying AIML. He also referred to the role of the UK governments Centre for Data Ethics and Innovation (CDEI) in the UKs regulatory framework for AI and, in particular to the CDEIs AI Barometer Report (June 2020), which has clearly identified several key areas that will most likely require regulatory attention, and some with significant urgency. These include:

In the absence of significant guidance, Mark provided a practical, 10-point, governance plan to assist firms in developing and deploying AI in the current regulatory environment, which is set out below. He highlighted the importance of firms keeping watch on regulatory developments, including what regulators and their representatives say about AI, as this may provide an indication of direction in the absence of formal advice. He also advised that firms ignore ethics considerations at their peril, as these will be central to any regulation going forward. In particular, for the reasons given above, he advised keeping up to date with reports from the CDEI. Other topics discussed in the session included lessons learnt for best practice in the fintech industry and how AI has been used to solve business challenges in financial markets.

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Current and future regulatory landscape for AI and machine learning in the investment management sector - Lexology

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September 20th, 2020 at 10:56 pm

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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 -…

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September 20th, 2020 at 10:56 pm

Posted in Machine Learning

When AI in healthcare goes wrong, who is responsible? – Quartz

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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.

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When AI in healthcare goes wrong, who is responsible? - Quartz

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September 20th, 2020 at 10:56 pm

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Is Wide-Spread Use of AI & Machine Intelligence in Manufacturing Still Years Away? – Automation World

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According to a new report by PMMI Business Intelligence, artificial intelligence (AI) and machine learning is the area of automation technology with the greatest capacity for expansion. This technology can optimize individual processes and functions of the operation; manage production and maintenance schedules; and, expand and improve the functionality of existing technology such as vision inspection.

While AI is typically aimed at improving operation-wide efficiency, machine learning is directed more toward the actions of individual machines; learning during operation, identifying inefficiencies in areas such as rotation and movement, and then adjusting processes to correct for inefficiencies.

The advantages to be gained through the use of AI and machine learning are significant. One study released by Accenture and Frontier Economics found that by 2035, AI-empowered technology could increase labor productivity by up to 40%, creating an additional $3.8 trillion in direct value added (DVA) to the manufacturing sector.

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However, only 1% of all manufacturers, both large and small, are currently utilizing some form of AI or machine learning in their operations. Most manufacturers interviewed said that they are trying to gain a better understanding of how to utilize this technology in their operations, and 45% of leading CPGs interviewed predict they will incorporate AI and/or machine learning within ten years.

A plant manager at a private label SME reiterates AI technology is still being explored, stating: We are only now talking about how to use AI and predict it will impact nearly half of our lines in the next 10 years.

While CPGs forecast that machine learning will gain momentum in the next decade, the near-future applications are likely to come in vision and inspection systems. Manufacturers can utilize both AI and machine learning in tandem, such as deploying sensors to key areas of the operation to gather continuous, real-time data on efficiency, which can then be analyzed by an AI program to identify potential tweaks and adjustments to improve the overall process.

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And, the report states, that while these may appear to be expensive investments best left for the future, these technologies are increasingly affordable and offer solutions that can bring measurable efficiencies to smart manufacturing. In the days of COVID-19, gains to labor productivity and operational efficiency may be even more timely.

To access this FREE report and learn more about automation in operations, download below.

Source: PMMI Business Intelligence, Automation Timeline: The Drive Toward 4.0 Connectivity in Packaging and Processing

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Is Wide-Spread Use of AI & Machine Intelligence in Manufacturing Still Years Away? - Automation World

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September 20th, 2020 at 10:56 pm

Posted in Machine Learning


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