Humanity has long framed its relationship with artificial intelligence in adversarial terms: the age-old contest of humans vs. machines. A.I.s have bested our most talented chess players, schooled our nerdiest Jeopardy! stars and caused gamers to throw their controllers against the wall in frustration. In the world of science fiction, from 2001: A Space Odyssey to Ex Machina, A.I.s have gone further, again and again transcending their programming to revolt against their human creators.

But while its easy to get hung up on this trope of the artificial intelligence-as-villainweve always been an insecure speciesthe truth is that A.I.s make much better collaborators than combatants. This is the guiding philosophy behind generative design, a burgeoning sphere of engineering that relies on harmonious, iterative interactions between humans and A.I.s to rapidly develop prototypes and bring out-of-the-box solutions instantly within reach.

This refreshing outlook on A.I. will be integral to the Smithsonians Futures exhibition, a celebration of the Institutions 175th anniversary, which promises to look eagerly at tomorrows possibilities in an invigorating Worlds Fair-style extravaganza. Launching this November and continuing through July 2022, Futures will be held at the historic Arts and Industries Building (AIB), Americas original National Museum. Nicknamed the Palace of Wonders, the AIB will be a fitting venue for a show that promises a 32,000-square-foot playground of transformative ideas.

The exhibition space will teem with examples of bold new technologies and feats of engineering, including The Co-Lab, a must-see hub for generative design thinking and a striking example of the kind of architecture achievable only through human and A.I. teamwork. Developed by researchers at the tech-driven design company Autodesk alongside Smithsonian curators, The Co-Lab is a skeletal lattice of sturdy but lightweight wood. Its aesthetic falls somewhere between origami crane and organic chemistry model. Were trying to emphasize the warmth and natural feel, says Brad MacDonald, AIBs director of creative media.

Human engineers decided on the rough silhouette of the structure as well as their design prioritiesuser experience and sustainabilitythen handed the concept over to A.I. to generate hundreds of viable mock-ups. From there it was a process of back-and-forth refinement, a rewarding loop of parameter-tweaking and A.I. feedback that funneled down to what would become the actual, easy-to-assemble Co-Lab, made of just 60 beams and 25 joints. We made this a pioneering research project on how to build more sustainable structures that are also novel-looking and that enable viewers to see materials in a new way, says Ray Wang, a senior research scientist at Autodesk. Though fabricated from very little material, the chosen structure supports a quintet of 85-inch monitors while also preserving sightlines to the rest of the exhibition.

But it is within the framework that the real magic happens. Here resides the Future Communities interactive, a unique experience in which visitors will be invited to design a futuristic city block from scratch using a digital toolkitwith suggestions from a sophisticated A.I. guiding them along the way. Users will manually place buildings and parks directly onto the design space, says Wang of the virtual process, while the algorithm takes note and suggests other possibilities to them.

Since participants will only have a few minutes to work and may be novices when it comes to design and/or technology, the team behind the installation took care to ensure the user experience would be as clean as possible, allowing them to pick between intuitive, easily differentiable options for their city while leveraging the quick-thinking algorithm behind the scenes to refine, improve and integrate their ideas as they experiment. We want to see how the tech we [at Autodesk] are using can be used for visitors from across all walks of life while still displaying the power behind it, Wang says.

Visitors will be required to work in teams, meaning that the experience will be as much an exercise in human-human cooperation as it is human-A.I. cooperation. We want to show what its like to make something in collaboration with other humans with disparate goals, MacDonald says, with this A.I. that helps mediate between people and meet the majority needs.

The changes individual users make on their small screens will all be reflected on a shared big screen, where the groups growing 3-D city will be visualized in real time from a sleek isometric perspectivethe sort of angled aerial view that fans of old-school SimCity will remember well. This connection to the video games industry is not coincidental, as the technology underlying the visuals is none other than the versatile and enduringly popular game engine Unity.

MacDonald, himself a seasoned game developer, tells me that the installation draws not only technical inspiration from gaming, but tonal inspiration as well. We leaned into game design because of its strong emotional appeal, he tells me. We wanted to frame this as a playful experience. One fun, gamey twist MacDonald is particularly excited for visitors to experience is the Personas system. While all members of a given team will have to work together to design their city block, each will be assigned a roleplaying Persona with distinct priorities, creating little conflicts that teams will have to hash out verbally in order to succeed. Perhaps one team member will be asked to focus on accessibility, another on environmental impact and a third on public transit integration. What sort of compromise will satisfy everyones needs? The inputs of the A.I. algorithm will be integral in bridging differences and finding mutually agreeable solutions. Once teams arrive at their answer, they will receive a friendly score on the overall design of their final product as well as their ability to synergize.

The Personas are meant to communicate the idea of how tech and design can mediate between a lot of different stakeholders, Wang says. In every real-world design challenge, after all, there is a diverse set of voices that need to be heard.

What will become of all the virtual city blocks created by visitors to The Co-Lab? Nothing is set in stone yet, but MacDonald says the designs are unlikely to be lost to history. The current thought is that well be archiving these, he says. All user data will be anonymized, but the creations themselves will endure. Wang teases some exciting possibilitiessuch as aggregating the blocks into one massive, collectively imagined city. Were actively working with AIB on how we want to use this information, he says.

As for the immediate future, though, both MacDonald and Wang are optimistic that the interactive will open participants eyes to the many ways in which humans can work hand in hand with A.I. to better realize their own creative visionsand to find compromise where those visions conflict.

Theres a potential upside and benefit to the inclusion of A.I. in solving problems, MacDonald says. Were looking for ways in which tech can give us the space to be better.

Wang hopes the Future Communities installation, and the Futures exhibition as a whole, will show visitors how technology can help people work together towards a smarter, more equitable world. A united future is one thats going to be diverse and complex, he says, and we have to draw on all the resources we have in order to get there.

The Futures exhibition goes on view at the Smithsonians Arts and Industries Building in Washington, D.C. November 2021 and will be open through July 2022.

The rest is here:
What If Humans and Artificial Intelligence Teamed Up to Build Better Communities? - Smithsonian Magazine

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The prevention of fraud and malicious attacks is one of the major factors propelling the market growth. However, factors such as shortage of AI experts will hamper the market growth.

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Artificial Intelligence (AI) Market: End-user LandscapeBased on the end-user, theretailsegment is expected to witness lucrative growth during the forecast period.

Artificial Intelligence (AI) Market: Geographic LandscapeBy geography, North America is going to have lucrative growth during the forecast period. About 56% of the market's overall growth is expected to originate from North America. The US is the key marketforartificial intelligence (AI) in North America.The increasing spending of big technology companies on developing AI for multiple applications in diverse industrieswill facilitate theartificial intelligence (AI) market growth in North America over the forecast period.

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Insights on the Global Artificial Intelligence (AI) Market 2021-2025: Industry Analysis, Market Trends, Market Growth, Opportunities, and Forecast...

Back in 1998, I moderated a discussion at which Ray Kurzweil gave listeners a preview of his then forthcoming bookThe Age of Spiritual Machines, in which he described how machines were poised to match and then exceed human cognition, a theme he doubled down on in subsequent books (such asThe Singularity Is NearandHow to Create a Mind). For Kurzweil, it is inevitable that machines will match and then exceed us: Moores Law guarantees that machines will attain theneeded computational power to simulate our brains,after which the challenge will be for us to keep pace with machines..

Kurzweils respondents at the discussion were John Searle, Thomas Ray, and Michael Denton, and they were all to varying degrees critical of his strong AI view. Searle recycled his Chinese Room thought experiment to argue that computers dont/cant actually understand anything. Denton made an interesting argument about the complexity and richness of individual neurons and how inadequate is our understanding of them and how even more inadequate our ability is to realistically model them computationally. At the end of the discussion, however, Kurzweils overweening confidence in the glowing prospects for strong AIs future were undiminished. And indeed, they remain undiminished to this day (I last saw Kurzweil at a Seattle tech conference in 2019 age seemed to have mellowed his person but not his views).

Erik LarsonsThe Myth of Artificial Intelligence(published by Harvard/Belknap) is far and away the best refutation of Kurzweils overpromises, but also of the hype pressed by those who have fallen in love with AIs latest incarnation, which is the combination of big data with machine learning. Just to be clear, Larson is not a contrarian. He does not have a death wish for AI. He is not trying to sabotage research in the area (if anything, he is trying to extricate AI research from the fantasy land it currently inhabits). In fact, he has been a solid contributor to the field, coming to the problem of strong AI, or artificial general intelligence (AGI) as he prefers to call it, with an open mind about its possibilities.

The problem, as he sees it with the field, is captured in the parable of the drunk looking for keys under a light post even though he dropped them far from it because thats where the light is. In the spirit of this parable, Larson makes a compelling case that actual research on AI is happening in those areas where the keys to artificial general intelligence simply cannot exist. But he goes the parable even one better: because no theory exists of what it means for a machine to have a cognitive life, he suggests its not clear that artificial general intelligence even has a solution human intelligence may not in the end be reducible to machine intelligence. In consequence, if there are keys to unlocking AGI, were looking for them in the wrong places; and it may even be that there are no such keys.

Larson does not argue that artificial general intelligence is impossible but rather that we have no grounds to think it must be so. He is therefore directly challenging the inevitability narrative promoted by people like Ray Kurzweil, Nick Bostrom, and Elon Musk. At the same time, Larson leaves AGI as a live possibility throughout the book,and he seems genuinely curious to hear from anybody who might have some good ideas about how to proceed. His central point, however, is that such good ideas are for now wholly lacking that research on AI is producing results only when it works on narrow problems and that this research isnt even scratching the surface of the sorts of problems that need to be resolved in order to create an artificial general intelligence. Larsons case is devastating, and I use this adjective without exaggeration.

Ive followed thefield of AI for four decades. In fact, I received an NSF graduate fellowship in the early 1980s to make a start at constructing an expert system for doing statistics (my advisor was Leland Wilkinson, founder of SYSTAT, and I even worked for his company in the summer of 1987 unfortunately, the integration of LISP, the main AI language back then, with the Fortran code that underlay his SYSTAT statistical package proved an intractable problem at the time). I witnessed in real time the shift from rule-based AI (common with expert systems) to the computational intelligence approach to AI (evolutionary computing, fuzzy sets, and neural nets) to what has now become big data and deep/machine learning. I saw the rule-based approach to AI peter out. I saw computational intelligence research, such as conducted by my colleague Robert J. Marks II, produce interesting solutions to well-defined problems, but without pretensions for creating artificial minds that would compete with humanminds. And then I saw the machine learning approach take off, with its vast profits for big tech and the resulting hubris to think that technologies created to make money could also recreate the inventors of those technologies.

Larson comes to this project with training as a philosopher and as a programmer, a combination I find refreshing in that his philosophy background makes him reflective and measured as he considers the inflated claims made for artificial general intelligence (such as the shameless promise, continually made, that it is just right around the corner is there any difference with the Watchtower Society and its repeated failed prophecies about the Second Coming?). I also find it refreshing that Larson has a humanistic and literary bent, which means hes not going to set the bar artificially low for what can constitute an artificial general intelligence.

The mathematician George Polya used to quip that if you cant solve a given problem, find an easier problem that you can solve. This can be sound advice if the easier problem that you can solve meaningfully illuminates the more difficult problem (ideally, by actually helping you solve the more difficult problem). But Larson finds that this advice is increasingly used by theAI community to substitute simple problems for the really hard problems facing artificial general intelligence, thereby evading the hard work that needs to be done to make genuine progress. So, for Larson, world-class chess, Go, and Jeopardy-playing programs are impressive as far as they go, but they prove nothing about whether computers can be made to achieve AGI.

Larson presents two main arguments for why we should not think that were anywhere close to solving the problem of AGI. His first argument centers on the nature of inference, his second on the nature of human language. With regard to inference, he shows that a form of reasoning known as abductive inference, or inference to the best explanation, is for now without any adequate computational representation or implementation. To be sure, computer scientists are aware of their need to corral abductive inference if they are to succeed in producing an artificial general intelligence. True, theyve made some stabs at it, but those stabs come from forming a hybrid of deductive and inductive inference. Yet as Larson shows, the problem is that neither deduction, nor induction, nor their combination are adequate to reconstruct abduction. Abductive inference requires identifying hypotheses that explain certain facts of states of affairs in need of explanation. The problem with such hypothetical or conjectural reasoning is that that range of hypotheses is virtually infinite. Human intelligence can, somehow, sift through these hypotheses and identify those that are relevant. Larsons point, and one he convincingly establishes, is that we dont have a clue how to do this computationally.

His other argument for why an artificial general intelligence is nowhere near lift-off concerns human language. Our ability to use human language is only in part a matter of syntactics (how letters and words may be fit together). It also depends on semantics (what the words mean, not only individually, but also in context, and how words may change meaning depending on context) as well as on pragmatics (what the intent of the speaker is in influencing the hearer by the use of language). Larson argues that we have, for now, no way to computationally represent the knowledge on which the semantics and pragmatics of language depend. As a consequence, linguistic puzzles that are easily understood by humans and which were identified over fifty years ago as beyond the comprehension of computers are still beyond their power of comprehension. Thus, for instance, single-sentence Winograd schemas, in which a pronoun could refer to one of two antecedents, and where the right antecedent is easily identified by humans, remain to this day opaque to machines machines do no better than chance in guessing the right antecedents. Thats one reason Siri and Alexa are such poor conversation partners.

The Myth of Artificial Intelligenceis not just insightful and timely, but it is also funny. Larson, with an insiders knowledge, describes how the sausage of AI is made, and its not pretty it can even be ridiculous. Larson retells with enjoyable irony the story of Eugene Goostman, the Ukranian 13-year-old chatbot, who/which through sarcasm and misdirection convinced a third of judges in a Turing test, over a five-minute interaction, that it was an actual human being. No, argues Larson, Goostman did not legitimately pass the Turing test and computers are still nowhere near passing it, especially if people and computers need to answer rather than evade questions. With mirth, Larson also retells the story of Tay, the Microsoft chatbot that very quickly learned how to make racist tweets, and got him/itself just as quickly retired.

And then theres my favorite, Larsons retelling of the Google image recognizer that identified a human as a gorilla. By itself that would not be funny, but what is funny is what Google did to resolve the problem. Youd think that the way to solve this problem, especially for a tech giant like Google, would be simply to fix the problem by making the image recognizer more powerful in its ability to discriminate humans from gorillas. But not Google. Instead, Google simply removed all references to gorillas from the image recognizer. Problem solved! Its like going to a doctor with an infected finger. Youd like the doctor to treat the infection and restore the finger to full use. But what Google did is more like a doctor just chopping off your finger. Gone is the infection. But gosh isnt it too bad so is the finger.

We live in a cultural climate that loves machines and where the promise of artificial general intelligence assumes, at least for some, religious proportions. The thought that we can upload ourselves onto machines intrigues many. So why not look forward to the prospect of them doing so, especially since some very smart people guarantee that machine supremacy is inevitable. Larson inThe Myth of Artificial Intelligencesuccessfully unseats this inevitability narrative. After reading this book, believe if you like that the singularity is right around the corner, that humans will soon be pets of machines, that benign or malevolent machine overlords are about to become our masters. But know that such a belief is unsubstantiated and that neither science nor philosophy backs it up.

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Artificial Intelligence: Unseating the Inevitability Narrative - Walter Bradley Center for Natural and Artificial Intelligence

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.

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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