Thanks to advanced technology, consumers can now access, spend, and invest their money in safer ways. Lenders looking to win new business should apply technology to make processes faster and more efficient.

Artificial intelligence has transformed the way we handle money by giving the financial industry a smarter, more convenient way to meet customer demands.

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Machine learning helps financial institutions develop systems that improve user experiences by adjusting parameters automatically. It's become easier to handle the extensive amount of data related to daily financial transactions.

Machine learning and AI are changing how the financial industry does business in these ways:

Fraud Detection

The need to enhance fraud detection and cybersecurity is no longer an option. People pay bills, transfer money, trade stocks, and deposit checks through smartphone applications or online accounts.

Many businesses store their information online, increasing the risk of security breaches. Fraud is a major concern for companies that offer financial services--including banks--which lose billions of dollars yearly.

Machine learning and artificial intelligence technologies improve online finance security by scanning data and identifying unique activities. They then highlight these activities for further investigation. This technology can also prevent credential stuffing and credit application fraud.

Cognito is a cyber-threat detection and hunting software impacting the financial space positively. Its built by a company called Vectra. Besides detecting threats automatically, it can expose hidden attackers that target financial institutions and also pinpoint compromised information.

Making Credit Decisions

Having good credit can help you rent an apartment of your choice, land a great job, and explore different financing options. Now more than ever, many things depend on your credit history, even taking loans and credit cards.

Lenders and banks now use artificial intelligence to make smarter decisions. They use AI to accurately assess borrowers, simplifying the underwriting process. This helps save time and financial resources that would have been spent on humans.

Data--such as income, age, and credit behavior--can be used to determine if customers qualify for loans or insurance. Machine learning accurately calculates credit scores using several factors, making loan approval quick and easy.

AI software like ZestFinance can help you to easily find online lenders, all you do is type title loans near me. Its automated machine learning platform (ZAML) works with companies to assess borrowers without credit history and little to no credit information. The transparent platform helps lenders to better evaluate borrowers who are considered high risk.

Algorithmic Trading

Many businesses depend on accurate forecasts for their continued existence. In the finance industry, time is money. Financial markets are now using machine learning to develop faster, more exact mathematical models. These are better at identifying risks, showing trends, and providing advanced information in real time.

Financial institutions and hedge fund managers are applying artificial intelligence in quantitative or algorithmic trading. This trading captures patterns from large data sets to identify factors that may cause security prices to rise or fall, making trading strategic.

Tools like Kavout combine quantitative analysis with machine learning to simultaneously process large, complex, unstructured data faster and more efficiently. The Kai Score ranks stocks using AI to generate numbers. A higher Kai Score means the stock is likely to outperform the market.

Online lenders and other financial institutions can now streamline processes thanks to faster, more efficient tools. Consumers no longer have to worry about unnecessary delays and the safety of their transactions.

About The Author:

Aqib Ijaz is a content writingguru at Eyes on Solution. He is adept in IT as well. He loves to write on different topics. In his free time, he likes to travel and explore different parts of the world.

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3 Applications of Machine Learning and AI in Finance - TAPinto.net

The European Union looks set to ban some of the most concerning forms of artificial intelligence, such as the social credit surveillance system used in China, according to draft AI regulations published by the bloc.

The proposed regulations, which will be reviewed by elected representatives before passing into law, will also bring some comfort to those outraged by instances of bias and discrimination generated by AI.

These include hiring algorithms found to systematically downgrade womens professional profiles and flawed facial recognition technology that has led police to wrongfully arrest black people in the United States. Such AI applications are regarded by the EU as high-risk and will be subject to tight regulations, with hefty fines for infringement.

This is the latest step in the European discussion of how to balance the risks and benefits of AI. The aim appears to be to protect citizens fundamental rights while maintaining competitive innovation to rival the AI industries in China and the US.

The regulations will cover EU citizens and companies doing business in the EU and are likely to have far-reaching consequences, as was the case when the EU introduced data regulations in 2018. The proposals are also likely to inform and influence the United Kingdom, which is currently developing its own strategic approach to this area.

Most strikingly, the draft legislation would outlaw some forms of AI that human rights groups see as most invasive and unethical. That includes a broad range of AI that could manipulate our behaviour or exploit our mental vulnerabilities as when machine-learning algorithms are used to target us with political messaging online.

Likewise, AI-based indiscriminate surveillance and social scoring systems will not be permitted. Versions of this technology are currently used in China, where citizens in public spaces are tracked and evaluated to produce a trustworthiness score that determines whether they can access services such as public transport.

The EU also looks set to take a cautious approach to a number of AI applications identified as high-risk. Among these technologies are large-scale facial recognition systems considered easy to deploy using existing surveillance cameras which will require special permission from EU regulators to roll out.

Many systems known to contain bias also classify as high-risk. AI that assesses students and determines their access to education will be tightly regulated such technology achieved notoriety after an algorithm unfairly determined UK students grades in 2020.

The same caution will apply to AI used for hiring purposes, such as algorithms that filter applications or evaluate candidates, as well as financial systems that determine credit scores. Similarly, systems that assess citizens eligibility for welfare or judicial support will require organisations to make detailed assessments to ensure they meet a new set of EU requirements.

To give it some teeth, and in line with the EUs existing punishment for serious data misuse, the AI regulations include fines for infringements of up to 20 million or 4% of global turnover, whichever is higher.

Globally unique and sweeping in its application, the proposed regulation is a clear statement from Europe that it prioritises citizens fundamental rights over technical autonomy and economic interests.

But there are also concerns. Some will argue the measures go too far, stifling Europes AI innovation. The White House in fact warned Europe not to overregulate AI in 2020, with the US aware that Chinas relative lack of protections could see it achieve a competitive advantage over its rivals.

On the other hand, privacy advocates and campaigners against bias in AI may be left disappointed. Some of the most problematic AI systems are excluded from the regulation, notably those used for military purposes, such as drones and other automated weapons again speaking to fears of Chinese dominance in weaponised AI.

It is also possible that other applications, such as the fusion of AI with existing mass surveillance capabilities, could be permitted where authorised by law. This would leave the door open for their use in law enforcement, which is exactly the area that some observers are most worried about. Such loopholes for AI-driven state surveillance systems will trouble human rights and privacy advocates.

Critics have highlighted the vague definition of AI detailed in the draft legislation, which focuses in particular on machine learning but may not apply to the next generation of computing technologies, such as quantum computing or edge computing. As always with legal documents, the devil will be in the detail.

Equally, there are open questions about the distinction between high-risk and low-risk AI. The regulations only apply to the former, but its not clear whether its always possible to determine the nature of AIs risks during the development cycle. Risk is a continuum, and a dichotomy between high and low will always require an arbitrary distinction which may cause problems down the line.

The regulation is no doubt a bold step in the right direction. It will now be reviewed by the European Council and the European Parliament. The process of reading, reviewing and agreeing will likely take some time, during which the questions raised here can be explored and attended to.

But it stands to reason that many of the building blocks of the regulation will persist. By standing firm against forms of invasive surveillance and bias-prone AI systems, the legislation is a strong reminder that Europe takes seriously its obligation to safeguard its citizens fundamental human rights in a period of disruptive technological change.

Bernd Carsten Stahl is Professor of Critical Research in Technology at the De Montfort University.

This article first appeared on The Conversation.

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Europe is set to ban artificial intelligence that is a threat to the safety and rights of people - Scroll.in

Hi. I know so many of you are applying for jobs right now sending in your resumes and wondering why didn't I hear back turns out a computer probably rejected you. Human being never even read it yet More and more employers are turning to robots to thin out the resume pile, deciding who moves forward and who doesn't based on certain things they come up with. So how can you beat the system? I'm getting you the inside tips, millions of resumes and cover letters uploaded to job postings on sites like CareerBuilder linked in and zip recruiter. Now employers are trying to stop a bottleneck of applications getting you into a job faster with artificial intelligence. That's right. In many cases computers, not people are scanning your resumes and looking for exactly what the employer wants from certain words and phrases to experience and descriptions. First tip don't just list your jobs right what you did and the skills you learned underneath emphasizing buzzwords the computer can pick up like increased and optimized as much of what you've worked on as you can put on a piece of paper to allow the technology do its work will really put you at an advantage. Next tip. Don't be afraid to talk numbers. If you worked on a project that brought in money listed increased sales by an average of 15 oversaw $1 million dollar marketing budget. Also avoid abbreviations. A computer might not be programmed to understand something like this are oh I so spell it out. Return on investment. Finally, did you have to take time off during covid to care for someone or maybe you were furloughed? Don't be embarrassed by the job gap included. Right? What you did and the new skills you picked up those experiences make you a better team member, making more creative. Bring a different aspect and perspective to whatever your role is. Okay, so your resume is getting noticed. Amazing. Now comes the interview and believe it or not. More and more companies are using robots to conduct the interviews as well. I know it sounds nuts but they have the computer recording your answers and analyzing your speech everything from your volume to your in donation to the phrases you choose. Let me show you how it works. One of the companies behind the interview software sent me a sample interview to take, tell us a little about yourself and what you are passionate about that. A lot, a lot of radio when I was a kid needless to say I didn't have a lot of friends but that led me to my real passion which is storytelling. You get three minutes to answer and then hit done. So it actually gives me the opportunity to re record it if I'm not happy with it. Like one of those old answering machines where you're like, I want to do it again. That's something you don't get to do in front of a live interviewer. So that's kind of cool. I keep going answering a couple more questions describe a time when you worked out an agreement with appear or team member, What did you do? What was the outcome? Describe a subject you learned quickly and one that took more time to learn. I hated math because there was a right and a wrong answer. The company behind this software is called Higher view and they gave me my results saying I did okay. But it turns out my answers were too short, not detailed enough. The computer didn't have enough to evaluate me on what are you looking for in these answers? How do I beat the box? So if I ask you a question about team orientation, you know, tell me about you a team you worked on in a very simple way. You know, as a simple example, it turns out the team oriented people use the word we more than they used, the word I we accomplish this. We did this together. It's very, very simple answer example, but that's sort of what we're looking at. Mm hmm. The tech companies making the software say this actually helps candidates in the end, they say some people want to interview in the morning, others in the afternoon. Maybe you feel your best at night. Now you can do the interview anytime you want and you have the chance to say that answer stunk. Let's do it again, which you obviously can't do in a one on one live interview. We have lots of bonus tips about how to maximize your chances to get that job on my website right now, check it out. It's important. Rossen reports dot com back to you

Rossen Reports: Tricks to land a job in the age of artificial intelligence

Updated: 3:01 PM CDT Apr 22, 2021

The job hunt is getting high-tech.Unemployment rates hit an all-time high during the pandemic, leaving many of you searching for new jobs. And companies are turning to artificial intelligence to sort through resumes and applications. So how can you make sure your application gets to the top of the pile? We're getting advice straight from the experts. Let's start with your resume. CareerBuilder CEO Irina Novoselsky says artificial intelligence will open the job search up for you behind the scenes. "It's no longer based on 'have you done it' but it now is based on 'can you do it'?" says Novoselsky. Computers can take your skillset and match you with jobs that match closely to what you can do. Websites like CareerBuilder can then offer you job openings at other companies or in other fields. Artificial intelligence creates algorithms that scan applications to identify candidates who match well with the job description.Tips for your resume:Don't just list the jobs you've had and when. Make sure you're writing what you did and the skills you gained underneath. "As much as what you've worked on, put it on a piece of paper to allow the technology to do its work. It will put you at an advantage," says Novoselsky.Include all hard and soft skills. If you can code in different languages, list the amount of languages and what they are. If you've worked in sales, customer service is a skill, so include it.Talk numbers. If you worked on project that brought in money or it has benefits that you can quantify, use the actual numbers. Using numbers on your resume shows employers what you have accomplished at work.Avoid abbreviations. A computer might not be programmed to understand an acronym or abbreviation.Leave off logos and pictures. Computers might not pick up special formatting. It could even add letters and symbols unintentionally.Did you have to take time off during the pandemic to care for someone? Or maybe you were furloughed? Include that job gap on our resume. Write what you did and snyy new skills you've picked up. "Those experiences make you a better team member, make you more creative, bring a different perspective to whatever your role is," says Novoselsky.If you're using a recruiting website, make sure your resume is set to public and not a private setting. If you set it to private, you're only asking to be alerted about the jobs you apply for. If your resume is set to public, the site can send you more matches on other jobs. It also makes you searchable. Employers can search for who they're looking for and your resume can pop up.New kinds of interviewsLet's talk about the interview. Artificial intelligence programs take a pre-recorded interview done by a job candidate. It will then analyze word and phrasing choices. Some programs analyze volume and intonation, measuring how enthusiastic you sound for the job opportunity. The program then lets the employer know which applicants ranked at the top based on the company's requirements. Based on the words you use, it's scoring you on things like communication, problem solving, etc. HireVue is a software company that provides video interviews for the company you might apply to. They provided me with a sample AI interview to take. I lined myself up in the camera and answered three sample questions that could be asked on an interview. I was given a few minutes to answer. If I didn't like my answer, I could re-record it. Nathan Mondragon, the chief industrial organizational psychologist with HireVue, explained that the three questions were designed to get to know me, measure my ability to work with teams and measure my willingness to learn. Mondragon said I did well but a few of my answers were too short. The computer needed more to evaluate me on. For example, I explained math was a difficult subject for me in school, but I never explained how I worked to get better at it. Kevin Parker, CEO of HireVue, says artificial intelligence is meant to analyze a candidate's words and phrasing. "If I ask you a question about team orientation, tell me about a team you worked on. Team oriented people use the word 'we' more than they use the word 'I.' It's a very simple answer but that's what we're looking at," says Parker. There are more benefits to pre-recording interviews for the candidate as well. You can record your interview at any time of the day and any day of the week. Parker says the design of the company is democratize the process, making it accessible to more people. "About 80% of our interviews take place outside of normal business hours. You're no longer constrained by the normal nine to five or Monday through Friday," says Parker. Another benefit is that these interviews can give a candidate feedback on how they did on a pre-recorded job interview.Both companies say the artificial intelligence is just the first step in the workflow for companies. Real people still go through resumes and applications, this is how they can do it at an efficient pace. Tips for your interview:Prepare for this like you would an in-person job interview. Read over the job description, research the company and practice to make sure you hit your talking points.Make sure your technology is in working order. Make sure you have a strong and reliable internet connection and don't forget to have your charger handy just in case.Lighting is key. Take a look at the picture and make sure there's no harsh shadows cast on your face.Take advantage of practice questions. Many of these sites, like HireVue, have demos you can request to take. This will get you more comfortable with the process.

The job hunt is getting high-tech.

Unemployment rates hit an all-time high during the pandemic, leaving many of you searching for new jobs. And companies are turning to artificial intelligence to sort through resumes and applications.

So how can you make sure your application gets to the top of the pile? We're getting advice straight from the experts.

Let's start with your resume. CareerBuilder CEO Irina Novoselsky says artificial intelligence will open the job search up for you behind the scenes. "It's no longer based on 'have you done it' but it now is based on 'can you do it'?" says Novoselsky. Computers can take your skillset and match you with jobs that match closely to what you can do. Websites like CareerBuilder can then offer you job openings at other companies or in other fields.

Artificial intelligence creates algorithms that scan applications to identify candidates who match well with the job description.

Tips for your resume:

Let's talk about the interview. Artificial intelligence programs take a pre-recorded interview done by a job candidate. It will then analyze word and phrasing choices. Some programs analyze volume and intonation, measuring how enthusiastic you sound for the job opportunity. The program then lets the employer know which applicants ranked at the top based on the company's requirements. Based on the words you use, it's scoring you on things like communication, problem solving, etc.

HireVue is a software company that provides video interviews for the company you might apply to. They provided me with a sample AI interview to take. I lined myself up in the camera and answered three sample questions that could be asked on an interview. I was given a few minutes to answer. If I didn't like my answer, I could re-record it.

Nathan Mondragon, the chief industrial organizational psychologist with HireVue, explained that the three questions were designed to get to know me, measure my ability to work with teams and measure my willingness to learn. Mondragon said I did well but a few of my answers were too short. The computer needed more to evaluate me on. For example, I explained math was a difficult subject for me in school, but I never explained how I worked to get better at it.

Kevin Parker, CEO of HireVue, says artificial intelligence is meant to analyze a candidate's words and phrasing. "If I ask you a question about team orientation, tell me about a team you worked on. Team oriented people use the word 'we' more than they use the word 'I.' It's a very simple answer but that's what we're looking at," says Parker.

There are more benefits to pre-recording interviews for the candidate as well. You can record your interview at any time of the day and any day of the week. Parker says the design of the company is democratize the process, making it accessible to more people. "About 80% of our interviews take place outside of normal business hours. You're no longer constrained by the normal nine to five or Monday through Friday," says Parker. Another benefit is that these interviews can give a candidate feedback on how they did on a pre-recorded job interview.

Both companies say the artificial intelligence is just the first step in the workflow for companies. Real people still go through resumes and applications, this is how they can do it at an efficient pace.

Tips for your interview:

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Rossen Reports: Tricks to land a new job in the age of artificial intelligence - 4029tv

Until recently, artificial intelligence sounded like something out of science fiction. But the technology of artificial intelligence (AI) is becoming increasingly common, from self-driving cars to e-commerce algorithms that seem to know what you want to buy before you do. Throughout the economy and many aspects of daily life, artificial intelligence has become the transformative technology of our time.

On April 21, 2021, Sanjay Patnaik, director of the Center on Regulation and Markets (CRM) at Brookings sat down with John R. Allen, president of the Brookings Institution, and Darrell M. West, vice president and director of Governance Studies at Brookings, for a fireside chat on their book, Turning Point: Policymaking in the Era of Artificial Intelligence. Drawing on findings and recommendations from Turning Point, they explored the risks and opportunities of artificial intelligence and discuss a policy blueprint for how to gain the benefits of artificial intelligence while reducing its potential disadvantages. This event was part of CRMs Reimagining Modern-day Markets and Regulations series, which focuses on analyzing rapidly changing modern-day markets and on how to regulate them most effectively.

Viewers submitted questions for speakers by emailing events@brookings.edu or via Twitter using #AIGovernance.

Turning Point: Policymaking in the Era of Artificial Intelligence is available to order in print and e-book on the Brookings Press page.

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Policymaking and artificial intelligence: A conversation with John R. Allen and Darrell M. West - Brookings Institution

Summary

After the presentation of a general European Approach to Artificial Intelligence by the EU Commission in March 2021, a detailed draft regulation aimed at safeguarding fundamental EU rights and user safety was published today (Draft Regulation). The Draft Regulations main provisions are the following:

The Draft Regulation applies to the placing on the market, putting into service, and use of AI Systems by Providers and Users in the EU, as well as other parties in specific cases.

The Draft Regulation includes a list of prohibited AI practices that are understood to contravene the EUs values and fundamental rights:

Natural persons must be notified when they are interacting with an AI System where it is not obvious from the circumstances and the context of the use. This obligation does not apply where the AI System is used to detect, prevent, investigate, or prosecute criminal offenses. Where AI Systems are used to generate audio or video content that resembles existing persons, objects, places, etc. (so-called deep fakes), the artificial creation of such content must be disclosed.

The Draft Regulation introduces three categories of High-Risk AI Systems and subjects Providers and Users as well as importers and distributors of such AI Systems to specific obligations. High-Risk AI Systems include:

The list is not conclusive. When the EU Commission identifies other AI Systems generating a high level of risk of harm, those AI Systems may be added to this list.

With regard to obligations linked to High-Risk AI Systems, the Draft Regulation provides for different sets of obligations for Providers, Users, importers, and distributors, respectively.

a. Providers obligations for High-Risk AI Systems

b. Users obligations for High-Risk AI Systems: Users must use High-Risk AI Systems in accordance with the instructions indicated by the Provider, monitor the operation for evident anomalies, and keep records of the input data.

c. Importers obligations for High-Risk AI Systems: Importers must, among other obligations, ensure that the conformity assessment procedure has been carried out and technical documentation has been drawn up by the Provider before placing a High-Risk AI System on the market.

d. Distributors obligations for High-Risk AI Systems: Distributors must, among other obligations, verify that the High-Risk AI System bears the required CE conformity marking and is accompanied by the required documentation and instructions for use.

e. Users, importers, distributors, and third parties becoming Providers: Any party will be considered a Provider and subject to the relevant obligations if it (i) places on the market or puts into service a High-Risk AI System under its name or trademark, (ii) modifies the intended purpose of the High-Risk AI System already placed on the market or put into service, or (iii) makes substantial modifications to the High-Risk AI System. In any of these cases, the original Provider will no longer be considered a Provider under the Draft Regulation.

The Draft Regulation provides for substantial fines in cases of non-compliance as follows:

It is expected that stakeholders will present various concerns and modification requests to the EU Commission which will likely cause a debated and challenging legislative process. We will keep you updated.

[View source.]

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Draft EU Regulation For Artificial Intelligence Proposes Fines Of Up To 6% Of Total Annual Turnover - JD Supra

Lawyers can review contracts and documents and extract vital information to organize data through applications such as Kira.

In a profession steeped in legislation, precedent, statutes and codes, there are new amendments and rulings constantly being released that have the potential to change the face of law. AI provides the ability to review and analyze such pertinent data quickly and easily, unshackling lawyers to better serve their clients.

According to Forbes, the legal services market is one of the largest in the world, grossing close to US$1-trillion a year globally. The report also notes that the field of law is tradition-bound and notoriously slow to adopt new technologies and tools. Still, Forbes predicts that to change in the coming years.

More than any technology before it, artificial intelligence will transform the practice of law in dramatic ways. Indeed, this process is already underway, according to the report. The law is in many ways particularly conducive to the application of AI and machine learning. Machine learning and law operate according to strikingly similar principles: they both look to historical examples to infer rules to apply to new situations.

There is little doubt AI has and will play a role in the practice of law. Still, it is important to be cognizant of the need for ethical guidelines.

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Measuring the advantages and obligations of artificial intelligence - Canadian Lawyer Magazine

Artificial intelligence is shaping the world. Learning how to defeat the coronavirus, automate cars, rollout robots are only some of the innovations that are changing the world. Increasing our dependence on medical innovations and customer service is driven by natural language systems became significantly more advanced. Quantum computing carries significance for AI since quantum computing can supercharge AI applications compared to binary-based classical computers.

Artificial Intelligence is the process of developing computers and robots capable of parsing data like a human being. Using machine learning, programmers can create methods that can teach machines how to rationalize, similar to the way humans think. Actions like learning, logic, reasoning, perception, creativity can be replicated by technology and used in every industry. Artificial intelligence works by giving a machine the inputs and letting the device develop its path to achieve its set goal. This type of program allows computers to optimize a situation and streamlines processes.

Within one year of the pandemic spread, Pfizer and Moderna, two healthcare companies, received approval from the U.S. Food and Drug Administration to release their COVID vaccines. It typically takes years, or decades, to develop a new vaccine. As early as March 2020, vaccine candidates to fight covid-19 were undertaking human tests, just a quarter after the first reported cases. The record speed of vaccine development was partly thanks to AI models. Computer models were evaluating all of the components of the COVID-19 virus. The analysis that AI models were considering was well beyond human abilities. There are tens of thousands of subcomponents to the outer proteins of a virus. Machine learning models can sort through this information and predict which subcomponents are the most likely to produce an immune response. The use of AI in vaccine development to fight the COVID-19 vaccine may revolutionize how all vaccines are created moving forward.

Share trading of Baidu rallied following the announcement in February that the company opened its LinearFold AI algorithm for scientific and medical teams. The outfit helps predict the secondary structure of the RNA sequence of a virus. LinearFold predicted the secondary structure of the SARS-CoV-2 RNA sequence in only 27 seconds, 120 times faster than other methods. The development of messenger RNA was the critical component of the vaccines. Instead of conventional approaches, which insert a small portion of a virus to trigger a human immune response, mRNA teaches cells how to make a protein that can prompt an immune response.

Fully automated driving continued to mature as companies continue testing driverless cars, trucks and opening up Robo-taxi services. Fully automatic driving, which enables rides without a human safety driver on board. The trucking business in many countries across the globe is a perfect testing ground for this artificial intelligence. Trucks on highways delivering from one destination to another present the perfect training ground. The day-to-day movement from different locations is removed, and highways are generally easier to navigate than city streets with pedestrians.

When it comes to cities, taxi services appear to be a good launching point. Baidu initiated the Apollo Go Robotaxi service in Changsha, Cangzhou, and Beijing, becoming the first company in China to start Robo-taxi trial operations in multiple cities. Baidus attempt at a Robo-taxi process will test the merits of AI systems and see if they can safely control a vehicle in complex road conditions and solve the majority of possible issues on the road, independent of a human driver.

Customer service in the wake of the pandemic saw an accelerated use of human language AI. Natural language systems experienced significant advances in processing aspects of human language like sentiment and intent. Natural language models are powering more accurate search results and more sophisticated chatbots and virtual assistants, leading to better user experiences. Companies are now using AI bots and chats as the first defense line as call centers were moved off campus to homes. If these processes are successful, it will provide the backdrop for cost-effective ways to interact with customers.

Artificial intelligence increased by leaps and bounds in the last 12-months. The highlight appears to be the introduction of RNA messenger viruses when this was needed more than ever. The future of fighting viruses will likely follow this method, avoiding the need to inject a dead or live virus into a human. Any variation in a virus is likely to be mapped by AI software, providing a cut and paste method to deal directly with variants. Human language methods also came at the right time. The pandemic affected the working environment at a time when customer service made significant advances. Once there is some normalcy to the work and living environment, the move to automated cars and trucks will likely significantly advance, paving the way for additional artificial intelligence.

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New Artificial Intelligence that is Shaping 2021 - Analytics Insight

Groupe Renault announces the appointment of Luc Julia, a worldwide expert in artificial intelligence and co-creator of the Siri technology, as Groupe Chief Scientific Officer.

The arrival of Luc Julia is great news for the company. His exceptional track record in artificial intelligence, data and object connectivity will be key to accelerating the deployment of our strategy and becoming a tech company that integrates vehicles.Renaulutionis all about talent, expertise and team inventiveness, and we are very pleased to welcome Luc at this moment of transformation for the company,said Luca deMeo.

I am very happy and proud to join Groupe Renaulttoday, the flagship of the French automotive industry. Im also happy to be starting theRenaulutionand to join teams that are building the automotive tech company of tomorrow. Together, drawing on my expertise in human-machine interface and IoT, we will develop new and unique experiences for our customers on and off-board and create value for the Groups brands,said Luc Julia.

Luc Julia will be responsible for supporting the functions and brands in the conception and deployment of the Groups roadmap for innovation and key technologies to meet the challenges of tomorrows mobility. In this capacity, he will act as an expert in fields as diverse as artificial intelligence, man-machine interfaces, connectivity and software. He will oversee the research and development of these technologies and innovations for their integration into the Groups product and service plan. He will also interface with key players and partners in the sector, notably in the framework of the Software Rpublique. In order to accelerate the companys shift towards a value chain more focused on next generation services and products, Luc Julia will also be responsible for instilling the Tech culture within the functions and brands.

SOURCE: Renault Group

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Luc Julia, world-renowned expert in artificial intelligence, joins Groupe Renault - Automotive World

Newswise The human immune defense is based on the ability of white blood cells to accurately identify disease-causing pathogens and to initiate a defense reaction against them. The immune defense is able to recall the pathogens it has encountered previously, on which, for example, the effectiveness of vaccines is based. Thus, the immune defense the most accurate patient record system that carries a history of all pathogens an individual has faced. This information however has previously been difficult to obtain from patient samples.

The learning immune system can be roughly divided into two parts, of which B cells are responsible for producing antibodies against pathogens, while T cells are responsible for destroying their targets. The measurement of antibodies by traditional laboratory methods is relatively simple, which is why antibodies already have several uses in healthcare.

"Although it is known that the role of T cells in the defense response against for example viruses and cancer is essential, identifying the targets of T cells has been difficult despite extensive research," says Satu Mustjoki, Professor of Translational Hematology.

AI helps to identify new key-lock pairs

T cells identify their targets in a key and a lock principle, where the key is the T cell receptor on the surface of the T cell and the key is the protein presented on the surface of an infected cell. An individual is estimated to carry more different T cell keys than there are stars in the Milky Way, making the mapping of T cell targets with laboratory techniques cumbersome.

Researchers at Aalto University and the University of Helsinki have therefore studied previously profiled key-lock pairs and were able to create an AI model that can predict targets for previously unmapped T cells.

"The AI model we created is flexible and is applicable to every possible pathogen - as long as we have enough experimentally produced key-lock pairs. For example, we were quickly able to apply our model to coronavirus SARS-CoV-2 when a sufficient number of such pairs were available," explains Emmi Jokinen, M.Sc. and a Ph.D. student at Aalto University.

The results of the study help us to understand how a T cell applies different parts of its key to identify its locks. The researchers studied which T cells recognize common viruses such as influenza-, HI-, and hepatitis B -virus. The researchers also used their tool to analyze the role of T-cells recognizing hepatitis B, which had lost their killing ability after the progression of hepatitis to hepatic cell cancer.

The study has been published in the scientific journalPLOS Computational Biology.

A new life for published data with novel AI models

Tools generated by AI are cost-effective research topics.

"With the help of these tools, we are able to make better use of the already published vast patient cohorts and gain additional understanding of them," points out Harri Lhdesmki, Professor of Computational Biology and Machine Learning at Aalto University.

Using the artificial intelligence tool, the researchers have figured out, among other things, how the intensity of the defense reaction relates to its target in different disease states, which would not have been possible without this study.

"For example, in addition to COVID19 infection, we have investigated the role of the defense system in the development of various autoimmune disorders and explained why some cancer patients benefit from new drugs and some do not", reveals M.D. Jani Huuhtanen, a Ph.D. student at the University of Helsinki, about the upcoming work with the new model.

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Artificial intelligence model predicts which key of the immune system opens the locks of coronavirus - Newswise

Ive been reviewing philosopher and programmer Erik LarsonsThe Myth of Artificial Intelligence.See my earlier posts,here,here,here,here,andhere.

Larson did aninteresting podcast with the Brookings Institution through its Lawfare Blogshortly after the release of his book. Its well worth a listen, and Larson elucidates in that interview many of the key points in his book. The one place in the interview where I wish he had elaborated further was on the question of abductive inference (aka retroductive inference or inference to the best explanation). For me, the key to understanding why computers cannot, and most likely will never, be able to perform abductive inferences is the problem ofunderdetermination of explanation by data. This may seem like a mouthful, but the idea is straightforward. For context, if you are going to get a computer to achieve anything like understanding in some subject area, it needs a lot of knowledge. That knowledge, in all the cases we know, needs to be painstakingly programmed. This is true even of machine learning situations where the underlying knowledge framework needs to be explicitly programmed (for instance, even Go programs that achieve world class playing status need many rules and heuristics explicitly programmed).

Humans, on the other hand, need none of this. On the basis of very limited or incomplete data, we nonetheless come to the right conclusion about many things (yes, we are fallible, but the miracle is that we are right so often). Noam Chomskys entire claim to fame in linguistics really amounts to exploring this underdetermination problem, which he referred to as the poverty of the stimulus. Humans pick up language despite very varied experiences with other human language speakers. Babies born in abusive and sensory deprived environments pick up language. Babies subject to Mozart from the womb and with rich sensory environments pick up language. Language results from growing up with cultured and articulate parents. Language results from growing up with boorish and inarticulate parents. Yet in all cases, the actual amount of language exposure is minimal compared to language ability that emerges and the knowledge of the world that results. On the basis of the language exposure, many different ways of understanding the world might have developed, and yet we seem to get things right (much of the time). Harvard philosopher Willard Quine, in his classicWord and Object(1960), struggled with this phenomenon, arguing for what he calledthe indeterminacy of translationto make sense of it.

The problem of underdetermination of explanation by data appears not just in language acquisition but in abductive inference as well. Its a deep fact of mathematical logic (i.e.,the Lwenheim-Skolem theorem) that any consistent collection of statements (think data) has infinitely many mathematical models (think explanations). This fact is reflected in ordinary everyday abductive inferences. We are confronted with certain data, such as missing documents from a bank safety deposit box. There are many, many ways this might be explained: a thermodynamic accident in which the documents spontaneously combusted and disappeared, a corrupt bank official who stole the documents, a nefarious relative who got access and stole the documents, etc.

But the et cetera here has no end. Maybe it was space aliens. Maybe you yourself took and hid the documents, and are now suffering amnesia. There are a virtually infinite number of possible explanations. And yet, somehow, we are often able to determine the best explanation, perhaps with the addition of more data/evidence. But even adding more data/evidence doesnt eliminate the problem because however much data/evidence you add, the underdetermination problem remains. You may eliminate some hypotheses (perhaps the hypothesis that the bank official did it, but not other hypotheses). But by adding more data/evidence, youll also invite new hypotheses. And how do you know which hypotheses are even in the right ballpark, i.e., that theyre relevant? Why is the hypothesis that the bank official took the documents more relevant than the hypothesis that the local ice cream vendor took them? What about the local zoo keeper? We have no clue how to program relevance, anda fortioriwe have no clue how to program abductive inference (which depends on assessing relevance). Larson makes this point brilliantly in his book.

You may also wish to read:

Are we spiritual machines? Are we machines at all? Inventor Ray Kurzweil proposed in 1999 that within the next thirty years we will upload ourselves into computers as virtual persons, programs on machines. The themes and misconceptions about computers and artificial intelligence that made headlines in the late 1990s persist to this day.

A critical look at the myth of deep learning Deep learning is as misnamed a computational technique as exists. The phrase deep learning suggests that the machine is doing something profound and beyond the capacity of humans. Thats far from the case.

Artificial intelligence understands by not understanding The secret to writing a program for a sympathetic chatbot is surprisingly simple We needed to encode grammatical patterns so that we could reflect back what the human wrote, whether as a question or statement.

Automated driving and other failures of AI How would autonomous cars manage in an environment where eye contact with other drivers is important? In cossetted and sanitized environments in the U.S., we have no clue of what AI must achieve to truly match what humans can do.

and

Artificial intelligence: Unseating the inevitability narrative. William Dembski: World-class chess, Go, and Jeopardy-playing programs are impressive, but they prove nothing about whether computers can be made to achieve AGI. In The Myth of Artificial Intelligence, Erik Larson shows that neither science nor philosophy back up the idea of an AI superintelligence taking over.

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Why Computers Will Likely Never Perform Abductive Inferences - Walter Bradley Center for Natural and Artificial Intelligence