Category Archives: Machine Learning

Associate Director Meisam Razaviyayn (L) and Director Murali Annavaram (R).

USC AND META COLLABORATE TO ESTABLISH THE USC-META CENTER FOR RESEARCH AND EDUCATION IN AI AND LEARNING

As with other new technologies, AI and Machine Learning have come to play an increasingly important role in our lives, however, there are many technological challenges to making them sustainable, energy efficient, and scalable to planetary scale demands. In an effort to address these challenges, advance AI research, and increase accessibility in AI education, the Ming Hsieh Department of Electrical and Computer Engineering and the Daniel J. Epstein Department of Industrial and Systems Engineering at the USC Viterbi School of Engineering together with Meta, have established the USC ECE-ISE Meta Center for Research and Education in AI and Learning.

Supporting a variety of activities, including open-source AI research and graduate scholarships, the center will be run by Murali Annavaram, Professor of Electrical and Computer Engineering, serving as Director and by Meisam Razaviyayn, Assistant Professor of Industrial and Systems Engineering serving as Associate Director.

This center will tackle the scaling and sustainability aspects of AI/ML systems as these technologies are deployed for solving planetary-scale challenges, said Annavaram. To this end we aim to advance our understanding of how AI algorithms interact with hardware, and to use this understanding in the design of energy efficient and open-source AI/ML systems of the future. Alongside open-source technology initiatives, the center will take initiatives to advance AI education equitably into the future. Said Razaviyayn, A major step in creating dependable AI systems is the development of reliable training mechanisms and responsible algorithms for modern world challenges. To this end, we believe that by equally supporting research and education, we will help bring about groundbreaking, fair, and trustworthy AI technology.

The center will support a variety of initiatives through Research, Fellowships, Curriculum, and Outreach activities. Initially the research themes will be centered on benchmarking and assessment technologies for AI algorithm-hardware platform interactions, and developing computational optimization algorithms for AI. These two areas of research are of vital importance to both the Epstein and the Ming Hsieh Departments, while also helping advance our work in AI in several ways, said Maged Dessouky, Chair of the Daniel J. Epstein Department of Industrial and Systems Engineering.

Producing consequential research will be coupled with rigorous educational training. The center will train a new generation of students who understand both the technical and the societal impacts of this important and pervasive new technology. I am excited to see USC and Meta come together to create the research center, said Bill Jia, Vice President of Engineering at Meta. The center will draw more students to understand AI and how it benefits and connects us all. With a focus on research in AI hardware, compilers, frameworks and algorithms, we can improve the performance, scalability, efficiency and productivity of AI.

I look forward to seeing a new generation of students take interest in helping to shape the future of AI and Machine Learning, said Vijay Rao, Director of Infrastructure at Meta. As we tackle the challenges we face today in AI it is essential that we invest in education and research in this growing field.

The center will support enhancing curricula and opportunities for hands-on laboratory training on AI and Machine Learning computing clusters for students in the MS program in Electrical and Computer Engineering-Machine Learning and Data Science, and in the MS in Analytics and other related programs. The former program provides students with focused, rigorous training in the theory, methods, and applications of data science, machine learning and signal and information process; the latter combines optimization, statistics, and machine learning to solve real problems in todays data-driven world.

These machines and the graduate courses they will help support are hugely useful to our department and we expect them to play a vital role in enhancing our ability to train the next generation of AI scientists, said Richard Leahy, Chair of the Ming Hsieh Department of Electrical and Computer Engineering.

Finally, the new center will pursue a variety of initiatives aimed at improving outreach to a diverse group of students. Some of the planned initiatives include summer internship programs and workshops to provide students with more hands-on ML system design experiences, as well as an annual symposium and poster session to give students better access to mentors and industry leaders. Diversity and inclusion are important values to USC Viterbi. Pursuing them is not only the right thing to do, but it also makes for better engineers and a better society, said Kelly Goulis, Sr. Associate Dean for Viterbi Admissions and Student Affairs of the Viterbi School of Engineering. Established programs in our office such as SURE (Summer Undergraduate Research Experience) and CURVE (Center for Undergraduate Research in Viterbi Engineering) address undergraduate research and outreach to diverse communities, thus helping also advance the outreach goals of the USC-Meta Center.

Published on December 17th, 2021

Last updated on December 17th, 2021

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USC and Meta Collaborate to establish the USC-Meta Center for Research and Education In AI and Learning - USC Viterbi | School of Engineering - USC...

A Christmas symposium from the British Neuroscience Association (BNA) has reviewed the growing relationship between neuroscience and artificial intelligence (AI) techniques. The online event featured talks from across the UK, which reviewed how AI has changed brain science and the many unrealized applications of what remains a nascent technology.

Opening the day with his talk, Shake your Foundations: the future of neuroscience in a world where AI is less rubbish, Prof. Christopher Summerfield, from the University of Oxford, looked at the idiotic, ludic and pragmatic stages of AI. We are moving from the idiotic phase, where virtual assistants are usually unreliable and AI-controlled cars crash into random objects they fail to notice, to the ludic phase, where some AI tools are actually quite handy. Summerfield highlighted a program called DALL-E, an AI that converts text prompts into images, and a language generator called gopher that can answer complicated ethical questions with eerily natural responses.

What could these advances in AI mean for neuroscience? Summerfield suggested that they invite researchers to consider the limits of current neuroscience practice that could be enhanced by AI in the future.

Integration of neuroscience subfields could be enabled by AI, said Summerfield. Currently, he said People who study language dont care about vision. People who study vision dont care about memory. AI systems dont work properly if only one distinct subfield is considered and Summerfield suggested that, as we learn more about how to create a more complete AI, similar advances will be seen in our study of the biological brain.

Another element of AI that could drag neuroscience into the future is the level of grounding required for it to succeed. Currently, AI models are provided with contextual training data before they can learn associations, whereas the human brain learns from scratch. What makes it possible for a volunteer in a psychologists experiment to be told to do something, and then just do it? To create more natural AIs, this is a problem that neuroscience will have to solve in the biological brain first.

The University of Oxfords Prof. Mihaela van der Schaar looked at how we can use machine learning to empower human learning in her talk, Quantitative Epistemology: a new human-machine partnership. Van der Schaars talks discussed practical applications of machine learning in healthcare by teaching clinicians through a process called meta-learning. This is where, said van der Schaar, learners become aware of and increasingly in control of habits of perception, inquiry, learning and growth.

This approach provides a potential look at how AI might supplement the future of healthcare, by advising clinicians on how they make decisions and how to avoid potential error when undertaking certain practices. Van der Schaar gave an insight into how AI models can be set up to make these continuous improvements. In healthcare, which, at least in the UK, is slow to adopt new technology, van der Schaars talk offered a tantalizing glimpse of what a truly digital approach to healthcare could achieve.

Dovetailing nicely from van der Schaars talk was Imperial College London professor Aldo Faisals presentation, entitled AI and Neuroscience the Virtuous Cycle. Faisal looked at systems where humans and AI interact and how they can be classified. Whereas in van der Schaars clinical decision support systems, humans remain responsible for the final decision and AIs merely advise, in an AI-augmented prosthetic, for example, the roles are reversed. A user can suggest a course of action, such as pick up this glass, by sending nerve impulses and the AI can then find a response that addresses this suggestion, by, for example, directing a prosthetic hand to move in a certain way. Faisal then went into detail on how these paradigms can inform real-world learning tasks, such as motion-tracked subjects learning to play pool.

One fascinating study involved a balance board task, where a human subject could tilt the board in one axis, while an AI controlled another, meaning that the two had to collaborate to succeed. After time, the strategies learned by the AI could be copied between certain subjects, suggesting the human learning component was similar. But for other subjects, this wasnt possible.

Faisal suggested this hinted at complexities in how different individuals learn that could inform behavioral neuroscience, AI systems and future devices, like neuroprostheses, where the two must play nicely together.

The afternoons session featured presentations that touched on the complexities of the human and animal brain. The University of Sheffields Professor Eleni Vasilaki explained how mushroom bodies, regions of the fly brain that play roles in learning and memory, can provide insight into sparse reservoir computing. Thomas Nowotny, professor of informatics at the University of Sussex, reviewed a process called asynchrony, where neurons activate at slightly different times in response to certain stimuli. Nowotny explained how this enables relatively simple systems like the bee brain to perform incredible feats of communication and navigation using only a few thousand neurons.

Wrapping up the days presentations was a lecture that showed an uncanny future for social AIs, delivered by the Henry Shevlin, a senior researcher at the Leverhulme Centre for the Future of Intelligence (CFI) at the University of Cambridge.

Shevlin reviewed the theory of mind, which enables us to understand what other people might be thinking by, in effect modeling their thoughts and emotions. Do AIs have minds in the same way that we do? Shevlin reviewed a series of AI that have been out in the world, acting as humans, here in 2021.

One such AI, OpenAIs language model, GPT-3, spent a week posting on internet forum site Reddit, chatting with human Redditors and racking up hundreds of comments. Chatbots like Replika that personalize themselves to individual users, creating pseudo-relationships that feel as real as human connections (at least to some users). But current systems, said Shevlin, are excellent at fooling humans, but have no mental depth and are, in effect, extremely proficient versions of the predictive text systems our phones use.

While the rapid advance of some of these systems might feel dizzying or unsettling, AI and neuroscience are likely to be wedded together in future research. So much can be learned from pairing these fields and true advances will be gained not from retreating from complex AI theories but by embracing them. At the end of Summerfields talk, he summed up the idea that AIs are black boxes that we dont fully understand as lazy. If we treat deep networks and other AIs systems as neurobiological theories instead, the next decade could see unprecedented advances for both neuroscience and AI.

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Ding Dong Merrily on AI: The British Neuroscience Association's Christmas Symposium Explores the Future of Neuroscience and AI - Technology Networks

Herzliya, Israel--(Newsfile Corp. - December 19, 2021) - Hexatone's FinanceAI offers Semi-Automated KYC verification that leverages artificial intelligence (AI) and its applications based on machine learning and Cognitive analysis to reduce the reliance on internal resources and manual processes.

Hexatone Financial Intelligence

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Hexatone's FinanceAI Features

Automating image quality checks

When a customer submits a poor-quality image, it can delay the KYC process by days or weeks as they have to upload new information. Computer vision algorithms can provide immediate feedback to the customer, allowing them to complete the image verification process in minutes rather than waiting.

Automatic verification

Object detection algorithms can automatically scan documents and check that all the relevant information is available. For example, if the customer fills in a form, it can validate that the data is correct without requiring a manual reviewer to do so.

Detecting fraud

Machine learning algorithms can analyze a vast number of transactions in seconds. The models can spot the signals of non-compliance and irregularities. Humans don't need to spend time manually sifting through transactions and flagging suspicious behaviour.

Automatic document digitization

When documents and images are verified, optical recognition models can extract data and enter it into back-office software systems. In the best-case scenario, the automation eliminates the need for manual data entry.

Omri Raiter, Co-Founder and Chief Technology Officer of Hexatone Finance says, "When implemented correctly, KYC automation by Hexatone's FinanceAI offers a significant boost to finance firms wanting to ensure regulatory compliance, and by improving their Customer Experience and overall business success."

What is the KYC Process?

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In financial services, the Know Your Customer (KYC) process includes all the actions firms need to take to ensure customers are genuine, assess, and monitor risks. The KYC process includes verifying ID, documents and faces with proof from the customer. All financial institutions must comply with KYC regulations to negate fraud and anti-money laundering (AML). Penalties will be applied if they fail to do so.

KYC Process

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Why is KYC so important?

Every year, it is estimated that between 2% and 5% of GDP is laundered, equal to around $2 trillion. KYC has become an essential part of AML regulations and processes to attempt to reduce that amount.

A KYC check helps to remove the risk associated with onboarding customers. They can assess whether people are involved in money laundering, fraud, or other criminal activities. People who are working with larger organizations or public figures, KYC is especially important as those people could be targets for bribery or corruption.

When financial firms don't get KYC right, they may face reputational damage as well as prosecution and fines. It's best practice to repeat the process regularly after onboarding, but it should be done at the acquisition stage as a minimum. A more regular KYC process can check for factors such as:

Spikes in an activity that might be a signal of criminal behaviour

Unusual cross-border activities

Reviewing the customer identity against government sanction lists

Adverse offline or online media attention

KYC is important to understand the customer account is up-to-date, the transactions match the original purpose of the account, and the risk level is appropriate for the type of transactions.

Who is KYC for?

Any financial institution that deals with customers during the process of opening and maintaining their accounts needs KYC in place. That includes banks, credit unions, wealth management firms, fintech companies, private lenders, accountants, tax firms, and lending platforms. Essentially, KYC regulations apply to any firm that interacts with money, which in the 21st century is pretty much all of them.

About Hexatone's FinanceAI

Hexatone's FinanceAI is an artificial intelligence-based solution for the Financial and banking sector. FinanceAI automatically evaluates the financial profiles of entities, companies, and their customers, enabling banks and financial institutes to make faster, better, and more business-relevant decisions. Using AI, machine learning, and cognitive analysis.

Media Contact

Company: Hexatone FinanceEmail: contactus@Hexatone.net

To view the source version of this press release, please visit https://www.newsfilecorp.com/release/108077

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Hexatone's FinanceAI Delivers the Power of Artificial Intelligence and Cognitive Analysis to the Financial Sector - Yahoo Finance

Hoch, Jannis M.; Sophie P. de Bruin; Halvard Buhaug; Nina von Uexkull; Rens van Beek & Niko Wanders (2021) Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach, Environmental Research Letters 16(12): 124068.

In the past decade, several efforts have been made toproject armed conflict risk into the future.

This study broadens current approaches by presenting a first-of-its-kind application of machine learning (ML) methods to project sub-national armed conflict risk over the African continent along three Shared Socioeconomic Pathway (SSP) scenarios and three Representative Concentration Pathways towards 2050. Results of the open-source ML framework CoPro are consistent with the underlying socioeconomic storylines of the SSPs, and the resulting out-of-sample armed conflict projections obtained with Random Forest classifiers agree with the patterns observed in comparable studies. In SSP1-RCP2.6, conflict risk is low in most regions although the Horn of Africa and parts of East Africa continue to be conflict-prone. Conflict risk increases in the more adverse SSP3-RCP6.0 scenario, especially in Central Africa and large parts of Western Africa. We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited compared to main conflict predictors but results suggest that changing climatic conditions may both increase and decrease conflict risk, depending on the location: in Northern Africa and large parts of Eastern Africa climate change increases projected conflict risk whereas for areas in the West and northern part of the Sahel shifting climatic conditions may reduce conflict risk. With our study being at the forefront of ML applications for conflict risk projections, we identify various challenges for this arising scientific field. A major concern is the limited selection of relevant quantified indicators for the SSPs at present. Nevertheless, ML models such as the one presented here are a viable and scalable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security.

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Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach Peace Research Institute Oslo - Peace...

Comput Intell Neurosci. 2021 Dec 17;2021:1916690. doi: 10.1155/2021/1916690. eCollection 2021.

ABSTRACT

BACKGROUND: From Ebola, Zika, to the latest COVID-19 pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities. People from low socioeconomic and educational backgrounds as well as low health literacy tend to be affected by the uncertainty, complexity, volatility, and progressiveness of public health crises and emergencies. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing and disseminating highly accessible, actionable, inclusive, coherent public health advice, which represent a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level.

OBJECTIVE: We aimed to translate the best practices of accessible, inclusive public health advice (purposefully designed for people with low socioeconomic and educational background, health literacy levels, limited English proficiency, and cognitive/functional impairments) on COVID-19 from health authorities in English-speaking multicultural countries (USA, Australia, and UK) to adaptive tools for the evaluation of the accessibility of public health advice in other languages.

METHODS: We developed an optimised Bayesian classifier to produce probabilistic prediction of the accessibility of official health advice among vulnerable people including migrants and foreigners living in China. We developed an adaptive statistical formula for the rapid evaluation of the accessibility of health advice among vulnerable people in China.

RESULTS: Our study provides needed research tools to fill in a persistent gap in Chinese public health research on accessible, inclusive communication of infectious diseases prevention and management. For the probabilistic prediction, using the optimised Bayesian machine learning classifier (GNB), the largest positive likelihood ratio (LR+) 16.685 (95% confidence interval: 4.35, 64.04) was identified when the probability threshold was set at 0.2 (sensitivity: 0.98; specificity: 0.94).

CONCLUSION: Effective communication of health risks through accessible, inclusive, actionable public advice represents a powerful tool to reduce health inequalities amidst health crises and emergencies. Our study translated the best-practice public health advice developed during the pandemic into intuitive machine learning classifiers for health authorities to develop evidence-based guidelines of accessible health advice. In addition, we developed adaptive statistical tools for frontline health professionals to assess accessibility of public health advice for people from non-English speaking backgrounds.

PMID:34925484 | PMC:PMC8683224 | DOI:10.1155/2021/1916690

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Developing Machine Learning and Statistical Tools to Evaluate the Accessibility of Public Health Advice on Infectious Diseases among Vulnerable People...

WealthTech(Technology) companies have rapidly spawnedinrecent years. Cutting-edgetechnologies are making their wayinto almost allindustries from manufacturing to logistics to financial services.

Within financial services,technologies such as data analytics, ArtificialIntelligence, Machine Learning among others are leading the wayinchanging business processes with faster turnaround time and superior customer experience.See Zee Business Live TV Streaming Below:

Astechnology evolves, business models must be changed to remain relevant. Thewealthmanagementsectorisalso notinsulated from this phenomenon!

Ankur Maheshwari CEO-Wealth, Equirus decodes the impact of newtechnology advancementsinthewealthmanagementindustry:

Wealthtechupscalingthewealthmanagementspace

Wealthtechaids companiesindelivering a more convenient, hassle-free and engaging experience to clients at a relatively low cost.

The adoption of new-agetechnologies such as big data analytics, ArtificialIntelligence (AI), and Machine Learning (ML) are helpingwealthmanagementcompanies stay ahead of the curveinthe new age ofinvesting.

While the adoption of advancedtechnologies has been underway for quite some time, the pandemic has rapidlyincreased the pace of the adoption oftechnology.

New ageinvestors and the young population are usingtechnologyina big way. Thisisevident from the fact that the total digital transactionsinIndia have grown from 14.59 billioninFY18 to43.71 billioninFY21 as reported by the RBI.

According to a report released by ACI Worldwide Globally, more than 70.3 billion real-time transactions were processedinthe year 2020, withIndia at the top spot with more than 25 billion real-time payment transactions.

Thisindicates the rising use oftechnology globally andinIndia within the financial servicesindustry.

There are various areas wheretechnology has had a significant impact on client experience and offerings ofwealthmanagementcompanies.

Client Meetings andInteractions

Inthe old days,wealthmanagers would physically meet theinvestors to discuss theirwealthmanagementrequirements. However, recently we see that a lot ofinvestors are demanding more digital touchpointswhichoffer more convenience.

Video calling and shared desktop features have been rapidly adopted by bothinvestors andwealthmanagers to provide a seamless experience.

24*7 digital touchpoints available

Technology has also enabled companies to provide cost-effective digital touchpoint solutions to clients that enable easier and faster access to portfolio updates, various reports such as capital gains reports, and holding statements and enable ease of doing transactions.

Features such as Chatbots and WhatsApp-enabled touchpoints are helpingindelivering a high-end client experienceina quick turnaround time.

Portfolio analytics and reporting

Data analytics has not only augmented the waywealthmanagers analyseinvestors portfolios but have also reduced time spent bywealthmanagers on spreadsheets.

WealthTechalso offers deeperinsightsinto the portfolioswhichassistwealthmanagersinproviding a more comprehensive and customized offering toinvestorswhichmatch their expectations and risk appetite.

ArtificialIntelligence and Machine Learningtechnologies combined with big data analytics are disruptingwealthmanagementspaceina big way. Robo-advisory and quant-based product offerings are making strong headwayinto thisspace.

Ease of process and documentation

Inthe earlier days, documentation and KYC process used to be a bottleneck with processing time goinginto several days as wellinsome cases. Storage of documentsisalso challenging as this requires safe storagespaceand documents are prone to damage and/or being misplaced.

With the advancementintechnologies, we are now moving towards a fully digital and/or phy-gital mode of operations. Whileinvestinginsome products like mutual funds the processiscompletely digital for other products like PMS, AIF, structures, etc. the processes are moving towards phy-gital mode.

The use of Aadhar based digital signature and video KYC have made it possible to reduce the overall processing time significantly!

Summing up:

A shift towards holistic offerings rather than product-based offering

Theincreasing young populationiscominginto the workforce and thereby creating a shiftinfocus towards new-ageinvestors.

These new-ageinvestors are not onlytech-savvy and early adopters oftechnology but are also demanding moreinterms of offerings.

With easy access toinformation and growing awareness,investors are looking for holistic offerings rather than merely product-based offeringswhichencompass all theirwealthmanagementneeds.

Incumbentsinthewealthmanagementspaceshould, if they havent already,incorporatetechnology as anintegral part of their client offering to stay relevant.

Forincumbents, it may prove to be cheaper and faster to getinto the tie-ups, partnerships, or acquire new agetechnology companies to quickly come up the curve rather than buildingin-housetechnology solutions.

As the adage goes, the only constantinlifeischange;technologyisa change for thewealthmanagementdomain that needs to be embraced!

(Disclaimer: The views/suggestions/advice expressed hereinthis article are solely byinvestment experts. Zee Business suggests its readers to consult with theirinvestment advisers before making any financial decision.)

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From AI to Machine Learning, 4 ways in which technology is upscaling wealth management space - Zee Business