Kate MacDonald, New Zealand Government Fellow at the World Economic Forum, and Lofred Madzou, Project Lead, AI and Machine Learning at the World Economic Forum have published a report that explains how AI can benefit everyone.

According to MacDonald and Madzou, artificial intelligence can improve the daily lives of just about everyone, however, we still need to address issues such as accuracy of AI applications, the degree of human control, transparency, bias and various privacy issues. The use of AI also needs to be carefully and ethically managed, MacDonald and Madzou recommend.

As mentioned in a blog post by MacDonald and Madzou:

One way to [ensure ethical practice in AI] is to set up a national Centre for Excellence to champion the ethical use of AI and help roll out training and awareness raising. A number of countries already have centres of excellence those which dont, should.

The blog further notes:

AI can be used to enhance the accuracy and efficiency of decision-making and to improve lives through new apps and services. It can be used to solve some of the thorny policy problems of climate change, infrastructure and healthcare. It is no surprise that governments are therefore looking at ways to build AI expertise and understanding, both within the public sector but also within the wider community.

As noted by MacDonald and Madzou, the UK has established many Office for AI centers, which aim to support the responsible adoption of AI technologies for the benefit of everyone. These UK based centers ensure that AI is safe through proper governance, strong ethical foundations and understanding of key issues such as the future of work.

The work environment is changing rapidly, especially since the COVID-19 outbreak. Many people are now working remotely and Fintech companies have managed to raise a lot of capital to launch special services for professionals who may reside in a different jurisdiction than their employer. This can make it challenging for HR departments to take care of taxes, compliance, and other routine work procedures. Thats why companies have developed remote working solutions to support companies during these challenging times.

Many firms might now require advanced cybersecurity solutions that also depend on various AI and machine learning algorithms.

The blog post notes:

AI Singapore is bringing together all Singapore-based research institutions and the AI ecosystem start-ups and companies to catalyze, synergize and boost Singapores capability to power its digital economy. Its objective is to use AI to address major challenges currently affecting society and industry.

As covered recently, AI and machine learning (ML) algorithms are increasingly being used to identify fraudulent transactions.

As reported in August 2020, the Hong Kong Institute for Monetary and Financial Research (HKIMR), the research segment of the Hong Kong Academy of Finance (AoF), had published a report on AI and banking. Entitled Artificial Intelligence in Banking: The Changing Landscape in Compliance and Supervision, the report seeks to provide insights on the long-term development strategy and direction of Hong Kongs financial industry.

In Hong Kong, the use of AI in the banking industry is said to be expanding including front-line businesses, risk management, and back-office operations. The tech is poised to tackle tasks like credit assessments and fraud detection. As well, banks are using AI to better serve their customers.

Policymakers are also exploring the use of AI in improving compliance (Regtech) and supervisory operations (Suptech), something that is anticipated to be mutually beneficial to banks and regulators as it can lower the burden on the financial institution while streamlining the regulator process.

The blog by MacDonald and Madzou also mentions that India has established a Centre of Excellence in AI to enhance the delivery of AI government e-services. The blog noted that the Centre will serve as a platform for innovation and act as a gateway to test and develop solutions and build capacity across government departments.

The blog post added that Canada is notably the worlds first country to introduce a National AI Strategy, and to also establish various centers of excellence in AI research and innovation at local universities. The blog further states that this investment in academics and researchers has built on Canadas reputation as a leading AI research hub.

MacDonald and Madzou also mentioned that Malta has launched the Malta Digital Innovation Authority, which serves as a regulatory body that handles governmental policies that focus on positioning Malta as a centre of excellence and innovation in digital technologies. The island countrys Innovation Authority is responsible for establishing and enforcing relevant standards while taking appropriate measures to ensure consumer protection.

Link:
AI and Machine Learning Technologies Are On the Rise Globally, with Governments Launching Initiatives to Support Adoption: Report - Crowdfund Insider

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.

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

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

Mischief can happen when AI is let loose in the world, just like any technology. The examples of AI gone wrong are numerous, the most vivid in recent memory being the disastrously bad performance of Amazon's facial recognition technology, Rekognition, which had a propensity to erroneously match members of some ethnic groups with criminal mugshots to a disproportionate extent.

Given the risk, how can society know if a technology has been adequately refined to a level where it is safe to deploy?

"This is a really good question, and one we are actively working on, "Sergey Levine, assistant professor with the University of California at Berkeley's department of electrical engineering and computer science, told ZDNet by email this week.

Levine and colleagues have been working on an approach to machine learning where the decisions of a software program are subjected to a critique by another algorithm within the same program that acts adversarially. The approach is known as conservative Q-Learning, and it was described in a paper posted on the arXiv preprint server last month.

ZDNet reached out to Levine this week after he posted an essay on Medium describing the problem of how to safely train AI systems to make real-world decisions.

Levine has spent years at Berkeley's robotic artificial intelligence and learning lab developing AI software that to direct how a robotic arm moves within carefully designed experiments-- carefully designed because you don't want something to get out of control when a robotic arm can do actual, physical damage.

Robotics often relies on a form of machine learning called reinforcement learning. Reinforcement learning algorithms are trained by testing the effect of decisions and continually revising a policy of action depending on how well the action affects the state of affairs.

But there's the danger: Do you want a self-driving car to be learning on the road, in real traffic?

In his Medium post, Levine proposes developing "offline" versions of RL. In the offline world, RL could be trained using vast amounts of data, like any conventional supervised learning AI system, to refine the system before it is ever sent out into the world to make decisions.

Also: A Berkeley mash-up of AI approaches promises continuous learning

"An autonomous vehicle could be trained on millions of videos depicting real-world driving," he writes. "An HVAC controller could be trained using logged data from every single building in which that HVAC system was ever deployed."

To boost the value of reinforcement learning, Levine proposes moving from the strictly "online" scenario, exemplified by the diagram on the right, to an "offline" period of training, whereby algorithms are input with masses of labeled data more like traditional supervised machine learning.

Levine uses the analogy of childhood development. Children receive many more signals from the environment than just the immediate results of actions.

"In the first few years of your life, your brain processed a broad array of sights, sounds, smells, and motor commands that rival the size and diversity of the largest datasets used in machine learning," Levine writes.

Which comes back to the original question, to wit, after all that offline development, how does one know when an RL program is sufficiently refined to go "online," to be used in the real world?

That's where conservative Q-learning comes in. Conservative Q-learning builds on the widely studied Q-learning, which is itself a form of reinforcement learning. The idea is to "provide theoretical guarantees on the performance of policies learned via offline RL," Levine explained to ZDNet. Those guarantees will block the RL system from carrying out bad decisions.

Imagine you had a long, long history kept in persistent memory of what actions are good actions that prevent chaos. And imagine your AI algorithm had to develop decisions that didn't violate that long collective memory.

"This seems like a promising path for us toward methods with safety and reliability guarantees in offline RL," says UC Berkeley assistant professor Sergey Levine, of the work he and colleagues are doing with "conservative Q-learning."

In a typical RL system, a value function is computed based on how much a certain choice of action will contribute to reaching a goal. That informs a policy of actions.

In the conservative version, the value function places a higher value on that past data in persistent memory about what should be done. In technical terms, everything a policy wants to do is discounted, so that there's an extra burden of proof to say that the policy has achieved its optimal state.

A struggle ensues, Levine told ZDNet, making an analogy to generative adversarial networks, or GANs, a type of machine learning.

"The value function (critic) 'fights' the policy (actor), trying to assign the actor low values, but assign the data high values." The interplay of the two functions makes the critic better and better at vetoing bad choices. "The actor tries to maximize the critic," is how Levine puts it.

Through the struggle, a consensus emerges within the program. "The result is that the actor only does those things for which the critic 'can't deny' that they are good (because there is too much data that supports the goodness of those actions)."

Also: MIT finally gives a name to the sum of all AI fears

There are still some major areas that need refinement, Levine told ZDNet. The program at the moment has some hyperparameters that have to be designed by hand rather than being arrived at from the data, he noted.

"But so far this seems like a promising path for us toward methods with safety and reliability guarantees in offline RL," said Levine.

In fact, conservative Q-learning suggests there are ways to incorporate practical considerations into the design of AI from the start, rather than waiting till after such systems are built and deployed.

Also: To Catch a Fake: Machine learning sniffs out its own machine-written propaganda

The fact that it is Levine carrying out this inquiry should give the approach of conservative Q-learning added significance. With a firm grounding in real-world applications of robotics, Levine and his team are in a position to validate the actor-critic in direct experiments.

Indeed, the conservative Q-Learning paper, which is lead-authored by Aviral Kumar of Berkeley, and was done with the collaboration of Google Brain, contains numerous examples of robotics tests in which the approach showed improvements over other kinds of offline RL.

There is also a blog post authored by Google if you want to learn more about the effort.

Of course, any system that relies on amassed data offline for its development will be relying on the integrity of that data. A successful critique of the kind Levine envisions will necessarily involve broader questions about where that data comes from, and what parts of it represent good decisions.

Some aspects of what is good and bad may be a discussion society has to have that cannot be automated.

More:
How do we know AI is ready to be in the wild? Maybe a critic is needed - ZDNet

Fort Collins, Colorado The report on the Machine Learning Market provides an in-depth assessment of the Machine Learning market including technological advancements, market drivers, challenges, current and emerging trends, opportunities, threats, risks, strategic developments, product advancements, and other key features. The report covers market size estimation, share, growth rate, global position, and regional analysis of the market. The report also covers forecast estimations for investments in the Machine Learning industry from 2020 to 2027.

The report is furnished with the latest market dynamics and economic scenario in regards to the COVID-19 pandemic. The pandemic has brought about drastic changes in the economy of the world and has affected several key segments and growth opportunities. The report provides an in-depth impact analysis of the pandemic on the market to better understand the latest changes in the market and gain a futuristic outlook on a post-COVID-19 scenario.

Global Machine Learning industry valued approximately USD 1.02 billion in 2016 is anticipated to grow with a healthy growth rate of more than 45.9 % over the forecast period 2017-2025.

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The report provides an in-depth analysis of the key developments and innovations of the market, such as research and development advancements, product launches, mergers & acquisitions, joint ventures, partnerships, government deals, and collaborations. The report provides a comprehensive overview of the regional growth of each market player.

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The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include:

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The report is designed with an aim to assist the reader in taking beneficial data and making fruitful decisions to accelerate their businesses. The report provides an examination of the economic scenario, along with benefits, limitations, supply, production, demands, and development rate of the market.

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For a better understanding of the global Machine Learning market dynamics, a regional analysis of the market across key geographical areas is offered in the report. The market is spread acrossNorth America, Europe, Latin America, Asia-Pacific, and Middle East & Africa.Each region is analyzed on the basis of the market scenario in the major countries of the regions to provide a deeper understanding of the market.

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The inception of Reports Globe has been backed by providing clients with a holistic view of market conditions and future possibilities/opportunities to reap maximum profits out of their businesses and assist in decision making. Our team of in-house analysts and consultants works tirelessly to understand your needs and suggest the best possible solutions to fulfill your research requirements.

Our team at Reports Globe follows a rigorous process of data validation, which allows us to publish reports from publishers with minimum or no deviations. Reports Globe collects, segregates, and publishes more than 500 reports annually that cater to products and services across numerous domains.

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Machine Learning Market to Witness Exponential Growth by 2020-2027 | Leading Players SAP SE , Sas Institute Inc. , Amazon Web Services, Inc. , Bigml,...

With experimentation and Amazon Personalize, customers can drive greater customer engagement and revenue

Optimizely, the leader in progressive delivery and experimentation, announced the launch of Optimizely for Amazon Personalize, amachine learning(ML) service from Amazon Web Services (AWS) that makes it easy for companies to create personalized recommendations for their customers at every digital touchpoint. The new integration will enable customers to use experimentation to determine the most effective machine learning algorithms to drive greater customer engagement and revenue.

Recommended AI News: Similarweb Adds New Chief Marketing and Technology Officers

Optimizely for Amazon Personalize enables software teams to A/B test and iterate on different variations of Amazon Personalize models using Optimizelys progressive delivery and experimentation platform. Once a winning model has been determined, users can roll out that model using Optimizelys feature flags without a code deployment. With real-time results and statistical confidence, customers are able to offer more touchpoints powered by Amazon Personalize, and continually monitor and optimize them to further improve those experiences.

Recommended AI News: Polyrize Announces Inaugural Shadow Identity Report

Until now, developers needed to go through a slow and manual process to analyze each machine learning model. Now, with Optimizely for Amazon Personalize, development teams can easily segment and test different models with their customer base and get automated results and statistical reporting on the best performing models. Using the business KPIs with the new statistical reports, developers can now easily roll out the best performing model. With a faster process, users can test and learn more quickly to improve key business metrics and deliver more personalized experiences to their customers.

Successful personalization powered by machine learning is now possible, says Byron Jones, VP of Product and Partnerships at Optimizely. Customers often have multiple Amazon Personalize models they want to use at the same time, and Optimizely can provide the interface to make their API and algorithms come to life. Models need continual tuning and testing. Now, with Optimizely, you can test one Amazon Personalize model against another to iterate and provide optimal real-time personalization and recommendation for users.

Recommended AI News: Suzy Online Shopping Study Says 86% of Consumers Will Shop Online Even Following the Pandemic

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New Optimizely and Amazon Personalize Integration Provides More - AiThority

With an all inclusive Machine Learning Chip market research report, comprehensive analysis of the market structure along with forecast of the various segments and sub-segments of the industry can be obtained. It also includes the detailed profiles for the Machine Learning Chip markets major manufacturers and importers who are influencing the market. A range of key factors are analysed in the report, which will help the buyer in studying the industry. Competitive landscape analysis is performed based on the prime manufacturers, trends, opportunities, marketing strategies analysis, market effect factor analysis and consumer needs by major regions, types, applications in global Machine Learning Chip market considering the past, present and future state of the industry.At present, the market is developing its presence and some of the Global Machine Learning Chip Marketkey players Involved in the study are Google Inc, Amazon Web Services, Inc., Advanced Micro Devices, Inc, BitMain Technologies Holding Company, Intel Corporation, Xilinx, SAMSUNG, Qualcomm Technologies, Inc., NVIDIA Corporation,

Machine learning chip market is expected to reach USD 72.45 billion by 2027 witnessing market growth with the rate of 40.60% in the forecast period of 2020 to 2027. Data Bridge Market Research report on machine learning chip market provides analysis and insights regarding the various factors expected to be prevalent throughout the forecast period while providing their impacts on the markets growth.

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What the Report has in Store for you?

Global Machine Learning Chip Market Dynamics:

Global Machine Learning Chip Market Scope and Market Size

Machine learning chip market is segmented on the basis of chip type, technology and industry vertical. The growth among segments helps you analyse niche pockets of growth and strategies to approach the market and determine your core application areas and the difference in your target markets.

Important Features of the Global Machine Learning Chip Market Report:

1) What all companies are currently profiled in the report?

List of players that are currently profiled in the report- Wave Computing, Inc., Graphcore, IBM Corporation, Taiwan Semiconductor Manufacturing Company Limited, Micron Technology, Inc., among other domestic and global players.

** List of companies mentioned may vary in the final report subject to Name Change / Merger etc.

2) What all regional segmentation covered? Can specific country of interest be added?

Currently, research report gives special attention and focus on following regions:

North America, Europe, Asia-Pacific etc.

** One country of specific interest can be included at no added cost. For inclusion of more regional segment quote may vary.

3) Can inclusion of additional Segmentation / Market breakdown is possible?

Yes, inclusion of additional segmentation / Market breakdown is possible subject to data availability and difficulty of survey. However a detailed requirement needs to be shared with our research before giving final confirmation to client.

** Depending upon the requirement the deliverable time and quote will vary.

How will this Market Intelligence Report Benefit You?

Global Machine Learning Chip Market Segmentation:

By Chip Type (GPU, ASIC, FPGA, CPU, Others),

Technology (System-on-Chip, System-in-Package, Multi-Chip Module, Others),

Industry Vertical (Media & Advertising, BFSI, IT & Telecom, Retail, Healthcare, Automotive & Transportation, Others),

Country (U.S., Canada, Mexico, Brazil, Argentina, Rest of South America, Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa)

New Business Strategies, Challenges & Policies are mentioned in Table of Content, Request TOC @ https://www.databridgemarketresearch.com/toc/?dbmr=global-machine-learning-chip-market

Table of Content:

Part 01: Executive Summary

Part 02: Scope of the Report

Part 03: Research Methodology

Part 04: Machine Learning Chip Market Landscape

Part 05: Market Sizing

Part 06: Customer Landscape

Part 07: Machine Learning Chip Market Regional Landscape

Part 08: Decision Framework

Part 09: Drivers And Challenges

Part 10: Machine Learning Chip Market Trends

Part 11: Vendor Landscape

Region wise analysis of the top producers and consumers, focus on product capacity, production, value, consumption, market share and growth opportunity in below mentioned key regions:

North America U.S., Canada, Mexico

Europe : U.K, France, Italy, Germany, Russia, Spain, etc.

Asia-Pacific China, Japan, India, Southeast Asia etc.

South America Brazil, Argentina, etc.

Middle East & Africa Saudi Arabia, African countries etc.

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The study objectives of this report are :

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Machine Learning Chip Market Comprehensive Analysis and Future Estimations with Top Key Players: Amazon Web Services, Inc., Advanced Micro Devices,...