Category Archives: Ai

For example, they trained 50 versions of an image recognition model on ImageNet, a dataset of images of everyday objects. The only difference between training runs were the random values assigned to the neural network at the start. Yet despite all 50 models scoring more or less the same in the training testsuggesting that they were equally accuratetheir performance varied wildly in the stress test.

The stress test used ImageNet-C, a dataset of images from ImageNet that have been pixelated or had their brightness and contrast altered, and ObjectNet, a dataset of images of everyday objects in unusual poses, such as chairs on their backs, upside-down teapots, and T-shirts hanging from hooks. Some of the 50 models did well with pixelated images, some did well with the unusual poses; some did much better overall than others. But as far as the standard training process was concerned, they were all the same.

The researchers carried out similar experiments with two different NLP systems, and three medical AIs for predicting eye disease from retinal scans, cancer from skin lesions, and kidney failure from patient records. Every system had the same problem: models that should have been equally accurate performed differently when tested with real-world data, such as different retinal scans or skin types.

We might need to rethink how we evaluate neural networks, says Rohrer. It pokes some significant holes in the fundamental assumptions we've been making.

DAmour agrees. The biggest, immediate takeaway is that we need to be doing a lot more testing, he says. That wont be easy, however. The stress tests were tailored specifically to each task, using data taken from the real world or data that mimicked the real world. This is not always available.

Some stress tests are also at odds with each other: models that were good at recognizing pixelated images were often bad at recognizing images with high contrast, for example. It might not always be possible to train a single model that passes all stress tests.

One option is to design an additional stage to the training and testing process, in which many models are produced at once instead of just one. These competing models can then be tested again on specific real-world tasks to select the best one for the job.

Thats a lot of work. But for a company like Google, which builds and deploys big models, it could be worth it, says Yannic Kilcher, a machine-learning researcher at ETH Zurich. Google could offer 50 different versions of an NLP model and application developers could pick the one that worked best for them, he says.

DAmour and his colleagues dont yet have a fix but are exploring ways to improve the training process. We need to get better at specifying exactly what our requirements are for our models, he says. Because often what ends up happening is that we discover these requirements only after the model has failed out in the world.

Getting a fix is vital if AI is to have as much impact outside the lab as it is having inside. When AI underperforms in the real-world it makes people less willing to want to use it, says co-author Katherine Heller, who works at Google on AI for healthcare: We've lost a lot of trust when it comes to the killer applications, thats important trust that we want to regain.

Link:

The way we train AI is fundamentally flawed - MIT Technology Review

The Crown has returned to Netflix with a host of new actors joining the star-studded cast. But a new AI analysis suggests not all of them have the royal touch required for their roles.

The system was developed by Largo Films, a Swiss firm that providesdata-driven analytics to the movie industry.The company used machine learning to predict whichof the thespians will capture the publics hearts and which ones will have them dreaming of the guillotine.

The AI analyzes movie scripts and footage from Largos database of films to create a DNA footprint for each actor comprised of more than 1,000 on-screen attributes, such as character typology and story elements

The system also knows how the audience reactionwas for those films from the financialresults and trend parameters,Sami Arpa, CEO of Largo, told TNW. From the analysis of over 400,000 films, it understands what are repeating successfulpatterns for each actor which helps us to create a DNA footprint of the actor.

[Read:Neurals market outlook for artificial intelligence in 2021 and beyond]

The system also creates a separate DNA footprint for the actual characters, such as Princess Diana. It then looks for correlations with the actors footprint to assess how well theyll fit the role.

It then calculates a final score for each actors suitability for their role. A score of over 90% indicates an excellent match, while one below 80% indicates a risky choice that could make viewers reach for the remote.

The casting of Gillian Anderson as milk snatcher Margaret Thatcher may have attracted the most headlines, but some critics have panned her performance. The BBCs Will Gompertz accused her of over-egging her Thatcher impression to such an extent she is close to unwatchable at times. This seems to align with Largos system, which gave the former X-Files star a lackluster 76.3%.

However, the breakout star Emma Corrin fared far better, with an impressive 90.6% score for her role as Princess Diana just ahead of Australian actor Elizabeth Debicki (88.1%), who will replace her from season five.

Olivia Coleman also performed well, with a score of 88.6% for her compatibility with Queen Elizabeth II. Her successor Imelda Stauton did even better, with 91.2%, but Largos top choice for the monarch is Ozark starJanet McTeer (94.1%).

Largo also analyzed the compatibility of the rumored cast for season five.

Speculation is rife thatDominic West will take over from Josh OConnor to play Prince Charles. But Largo gave him a score of just 69.8%, way behind their top pick of Poldark heartthrob Aidan Turner (96.1%).

Well find out soon whether The Crownscasting team agrees with the AIs analysis.

Published November 20, 2020 14:20 UTC

Link:

This AI is unimpressed by Gillian Andersons casting in The Crown - The Next Web

Artificial intelligence (AI) is increasingly transforming business processes and strategies across industry sectors. Companies are figuring out how to take advantage of AI, focusing primarily on customers' needs and experience. However, AI could also be a powerful tool to enhance internal decision-making processes and governance through advanced analytical models and robust risk-management methods.

Research is progressing apace, and some pioneering businesses are starting to adopt AI-based technology also for internal management purposes. Yet the breadth and depth of these changes and the resulting legal implications are yet to be fully explored.

How AI may enhance decision-making processes

AI is a broad field that encompasses a variety of emerging technologies and areas of research, ranging from computers or other problem-solving machines or robots to machine learning or deep learning methods.

Depending on the sophistication of the AI tool, AI can contribute to decision-making processes by:

AI in the boardroom

There are several ways in which AI could be deployed by directors and top managers in making key business or governance decisions.

AI can materially increase the quality of the information on which board resolutions are based. It could be used to gather, develop and produce information on the valuation of potential targets in the M&A process, develop economic predictions on the launch of new business lines or products, or assist the directors in identifying the best bidding or project management strategy in an auction context.

AI can also help directors in monitoring and anticipating business risks through advanced risk-management tools. Financial institutions could significantly improve the internal assessment and on-going testing of their balance sheet quality, capital adequacy and risk exposure through dedicated algorithms. AI can also be used to monitor and predict changes in customers' preferences, anticipating the need to adapt the company's business strategies accordingly.

Directors could benefit from the use of AI by tracking the capital allocation patterns of competitors to spot areas of improvement (e.g., in terms of R&D investments) or alternative business strategies. AI can identify potential new competitors moving into key product markets, allowing directors to take actions to protect the market share of their company.

Decision making by directors as fiduciaries

Making a decision is the very essence of the directors' responsibilities. Due to its collective nature, the decision-making process of a board is significantly more complex than that of individuals, as it requires the cooperation of multiple persons with potentially conflicting interests, cultures and perspectives.

More importantly, while making decisions as part of the board, directors are required to act as fiduciaries in the interest of their constituencies, which may include the shareholders, the company or other stakeholders at large, depending on the nuances of the law.

Legal systems have historically addressed the complexities surrounding board decisions by detailing the procedural rules applying to board meetings and spelling out the fiduciary duties of directors.

In several jurisdictions, directors have a duty of care towards the company, which requires them, among others, to act diligently, on an informed basis and in the absence of conflicts. Directors must also ensure that the company has a proper internal organization and governance system in place. Failure to comply with such duties may lead to the removal of directors as well as personal liability.

At the same time, directors have some discretion to make their decisions without being subject to scrutiny in accordance with the "business judgment rule" standard, which is followed by courts in various jurisdictions. Under the business judgment rule, directors generally cannot be held liable forbad business decisions, if these were taken upon a proper and careful evaluation of the information gathered and appropriately motivated (i.e., not self-interested). Courts can scrutinize the merits of directors' decisions if they were conflicted, acted with reckless indifference, disregarded the company interest, were unreasonable or not properly informed, or did not give any motivation supporting their judgment.

Legal issues and areas of attention

AI certainly offers significant opportunities to bolster internal business processes. However, AI does not present an opportunity for directors to avoid their fiduciary duties. Accordingly, directors should carefully assess how to use AI consistently with their fiduciary duties.

It is unclear whether and to what extent AI will be capable of balancing conflicting interests (e.g., between customers and employees, etc.) while assessing the merits of different business decisions, or to factor in legal uncertainties as regards the extent and scope of the duties that directors must discharge on a case-by-case basis.

In addition, a typical problem of machine-learning AI processes is the difficulty of understanding the nexus between the output of the algorithm and the inputs of the user (so-called "black box"). This could undermine the protection afforded by the business judgment rule if decisions are taken on the basis of such output, as the "random" component of machine-learning processes may conflict with the duty to act on an informed basis and motivate board decisions, after due enquiry and any ad hoc exchange of views with proper peers within the same organization and also with their main clients/suppliers.

It might well be, on the other hand, that the use of AI will become one of the parameters to assess whether directors acted on an informed basis, and that AI tools will routinely be used in board meetings to analyze Big Data in real time.

Conclusions

Because of on the scope and application of directors' fiduciary duties, AI is unlikely to replace human judgment in boardrooms. Directors could be required by courts to test the outcomes of AI processes on the basis of their own judgment and additional information or advice. The question will be how directors could actually discharge this task, considering the amount of data that AI may process and how sophisticated algorithms can be compared to human judgment.

See more here:

AI in the boardroom: opportunities and challenges - Lexology

A solar-powered autonomous drone scans for forest fires. A surgeon first operates on a digital heart before she picks up a scalpel. A global community bands together to print personal protection equipment to fight a pandemic.

The future is now, says Frdric Vacher, head of innovation at DassaultSystmes. And all of this is possible with cloud computing, artificial intelligence (AI), and a virtual 3D design shop, or as Dassault calls it, the 3DEXPERIENCE innovation lab. This open innovationlaboratoryembraces the concept of the social enterpriseand merges collective intelligence with a cross-collaborativeapproach by buildingwhat Vacher calls communities of peoplepassionate and willing to work together to accomplish a common objective.

This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Reviews editorial staff.

Its not only software, it's not only cloud, but its also a community of peoples skills and services available for the marketplace,Vachersays.

Now, because technologies are more accessible, newcomers can also disrupt, and this is where we want to focus with the lab.

And for DassaultSystmes, theres unlimited real-world opportunities with the power of collective intelligence, especially when you are bringing togetherindustry experts, health-care professionals, makers, and scientists totackle covid-19. Vacher explains, We created an open community, Open Covid-19, to welcome any volunteer makers, engineers, and designers to help, because we saw at that time that many people were trying to do things but on their own, in their lab, in their country. This wasted time and resources during a global crisis. And, Vacher continues, the urgency of working together to share information became obvious, They were all facing the same issues, and by working together, we thought it could be an interesting way to accelerate, to transfer the know-how,andto avoid any mistakes.

Business Lab is hosted by Laurel Ruma, director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next.

This episode of Business Lab is produced in association with DassaultSystmes.

How Effective is a Facemask? Heres a Simulation of Your Unfettered Sneeze, by Josh Mings, SolidSmack, April 2, 2020

Open COVID-19 Community Lets Makers Contribute to Pandemic Relief, by Clare Scott, The SIMULIA Blog, Dassault, July 15, 2020

Dassault 3DEXPERIENCE platform

Collective intelligence and collaboration around 3D printing: rising to the challenge of Covid-19, by Frdric Vacher, STAT, August 10, 2020

LaurelRuma:From MIT Technology Review, Im Laurel Ruma. And this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic today isaccelerating disruptive innovations to benefit society by building and running massive simulations. The world has big problems, and its going to take all of us to help solve them. Two words for you:collective intelligence.

My guest isFrdric Vacher, who is the head of innovation atDassaultSystmes. He is a mechanical engineer who has had a long career at Dassault. First, leading the partnership program, and then launching the 3DEXPERIENCELab. This episode of Business Lab is produced in association with DassaultSystmes. Frdric, welcome to Business Lab.

Frdric Vacher:Good morning,Laurel. Good morning, everyone.

Laurel:Could you start by first telling us a bit about DassaultSystmes? I dont want listeners to be confused with the aviation company, because were talking about a 3D modeling and simulation enterprise that was founded almost 40 years ago and has more than 20,000 employees around the globe.

Frdric:Yeah,that istrue. We are DassaultSystmes, the 3D experience company. Wehave beendigital since day one.Dassault Aviation is one of our clientslike all the aerospace companiesbut our customersare alsocar,shipbuilding, consumer goods, consumer packaged goods companies, and so on. We are a worldwide leaderinproviding digital solutions, from design simulation to productionandwe cover 11 industries. Our purpose is toharmonize product,nature,andlife.

For the past two years,wehavehelpedourclients industry roles to innovate by digitalizing and engineering their products from very complex productstosimplerproducts.Forthe past10 years, we have invested very stronglyintwo directions: the nature of life and from things,to life.

Laurel:That is a complicatedkind of process to sort of imagine. Butwiththe 3DEXPERIENCE Lab, scientists and engineers can go in and build these cloud-based simulations for 3D modeling, digital twins, and product in a waythat isreally collaborative, taking advantage of that human system. Could you talk to us a bit more about whyDassaultfelt it was important to create this 3DEXPERIENCE Lab in a way that was so collaborative?

Frdric:We started the 3DEXPERIENCE Lab initiative five years ago to accelerate newcomers, very small actors, startups, makers, as we believe that innovation is everywhere. For 40 years,weinnovatedwith the aerospace and defense industry.[For example] we established a partnershipwith Boeing onthe 777, for instance, the first airplane that was fully digitized [made into a digital twin].Andnot only the product, but all the processes andthe factories.Now, because technologies are more accessible, newcomers can alsodisrupt,and this is where we want to focuswiththe lab. This lab is targeting open innovation with startup acceleratorsempoweringcommunities online, communities of people,passionate andwilling to work togethertoaccomplish a common objective.

Laurel:And because it is an open lab, anyone can participate. Butyou havecreated a specific program for startups. Could you tell us more about that program?

Frdric:Since the beginning,we havebeen identifyingand sourcing startups that providea strong impactto societyas a disruptive product or projectwanting tomakea real impact.Thisprogram provides those startupsaccessto our softwareand professional solutions that the industryisusing intheirday-to-dayactivities.But also funds to this cloud platform forcommunities to createaccess to mentors.Mentorswill help them acceleratein their development, providing know-howandknowledge.

Laurel:Thatkind of access for startups is rather difficult to get, right? Because this kind of software is professional grade, it is expensive. They may not be able to afford it or understand that they even have access to it. But interestingly, it's not just the software companies and startupsthatwould have access to it's also the people who work at Dassault, correct?

Frdric:Yeah,that iscorrect. Thanksto this3DEXPERIENCE platform in thecloud,as you mentioned,we have 20,000 people worldwide in 140 countries. Those people are knowledgeable astheysupport their business in many industries in terms of technologyandinscience.Andthose people onavolunteeringbasis,could join a lab projectascoach,mentor,orstartup.Thankstothese cloud platforms,theyarenot only discussingandproviding some insight or information orguidance,buttheycanreally co-design with those guys.LikeaGoogle document, many peoplecanwork on the same documentwhilebeing in different locations.This program enables us to perform the same way but on a digital mock-up.

Laurel:People can kind of really visualize what you have in mind. The 3DEXPERIENCE Lab does two things. One, it creates a way for an enterprise to build an entire product as a3D vision,incorporating feedback from the research lab, the factory floor, and the customer.So,all ofthe stakeholders can work in a single environment. Could you give us an example of that and how that works?

Frdric:In a single environment in thecloudthey can startwithusing some apps, maybe fromCATIA or SolidWorks.They can do the engineering part of the job on the same data model theywoulduse toperform their own simulations. Any type of digitalsimulation thatwill help those guys to announce the engineering and the design of their product.Through that,they will optimize the design and then go to the manufacturing aspect,delivering all the processes needed up to programming the machines. Butthat is, I would say, the standard way to operate.

Now for this platform, you have access also to servicesformarketplace.Thisis particularly interesting forearly-stagestartups as they struggle to find the right partnerorthe right supplier to manufacture something. Here, at one click of a button, they can source components from millions of components that are available through qualified suppliers online. Just drag and drop thecomponentinto theproject.

They can access to thousands of factories worldwide, whereby, they will be able to producetheir parts for thosefactories, managing all the business online between the two suppliers. And then, they may alsohaveaccess to engineering services,where ifyou want to do something, but you dont have the skills to do it, then youcontract thejob to a service bureauorqualified partners that could deliver the job. So, itsnot only software, its not only cloud, but it's alsoacommunity of people's skillsand services available forthemarketplace.

Laurel:And it really is a platform,right? To directly offer services and innovation from one company to another, in a way that's very visual and hands-on, so you couldactually almostdemo the product before you buy it because you are in this 3DEXPERIENCE environment. How does that work? Withan example from a company?Am I thinking of that in the correct way?

Frdric:Youre correct. The complete digital project is done on the platform before the real product is produced. You want to develop a new car or a new table or a new chair or new lamp, you design everything in 3D. You simulate to make itrobustand then you do the engineering to make sure that the manufacturing would be fine based on your manufacturing capacities or partners.And,you go one step further on that, then you can really produce the marketing operations, produce the advertising, thehigh qualitypictures you need for your flyers, even the experience from the video torecordthe commercials. So,the digital assets thataredone alreadyat the beginning of the project, to engineer a new product,arenow used not only for production,but also for communication, marketing, training, and so on. That means thatthose people in your marketing department can do the job in paralleland perform all their deliverables, even if the product, the physical product, is not thereyet.

Laurel:How do companies feel about sharing some of this intellectual property ahead of time before the product is even developed? You must have to have very special philosophies and outlooks to want to do this, right?

Frdric:Yeah. The IP is very important for us and obviously for our clients. We deliver to each client a dedicated platform so that they are in a 100% secure network environment. This is true for the big guys like Boeing, Airbus in the aerospace industry or BMW, Tesla in the auto industry.But its also true for smaller startupslikewe are talking about with this innovation lab.

Laurel:Thesystem really does bring togetheranenormousamountof complicated issues, including cybersecurity, as well as processing power, data science, artificial intelligence, but also that human intelligence. How does Dassault define collective intelligence?Whyis that so important as a philosophy?

Frdric:Its key. Behind any project, behind any companies, you have people, right?This is whyon this platform, on the baseline services, you have all those services to enable people to collaborate, not only to manage their project with sequences,with milestones, with task management and so on like any corporation would do, but now in a very agile way for communities. To connect people, to help people, to workbettertogether to match skills and needs. This is a new approach, obviouslythisapproachisnew for professionals, but these services were brought by social networks to thegeneral publicmany yearsago,butwe applied these services to innovative processes onto engineering processes within a company.

Laurel:You mentioned skills,andI thinkaninteresting place to kind of look at it fora bit,is how do people transfer knowledge? And is this environment conducive to training and helping perhaps one group teach the other group how to perform basic tasks or understand a product better? Are you seeing that when companies work with the platform, theyactually bringin everyone,including marketing?So, everyone can have a much better understanding of the entire product?

Frdric:Definitely.First,we share acommonreferential.So, there is no loss in email exchange, in data exchange, and so on. Everyones work aroundadigital twin of the projectis accurateandup-to-date. Second,this platform enablesyouto capitalize knowledgeandknow-how andit isvery important, especially when seniors are retiring,to transferthe knowledgeto new generations.We have seen in the past, especially in the aerospace industry, manyfellows,whohave left theircompany haveto come back to the company because theyareseen ascritical in the process with their knowledge.Such a platform now allows companies to keeptheknowledge inside and to transferthe knowledgefrom one generation to another.

Laurel:So that idea of collective intelligence really does spread throughout an entire enterprise. The lab does take ona number ofthemes, including healthcare. Could you talk about a few of those ideas?

Frdric:Yeah. With the lab, as I said,we have main criteriatoselectaproject:a strong, positive impacttosociety and a disruptive projectthat callsfor collective intelligence. We are very selective as we really want to think big.Wewant to accelerateabout 10 projects a yearonaglobal standpoint. We heavilyuse data intelligence and our toolsto scanand toscrollthroughall news on the web, newVCs, the founding anew startup, [all of this is done]in order to understand the weak signals, the new trends,andbe abletoidentifythosenewer innovators. We use the same platform to orchestrate this ideation process.Havinga smallidea,nurturing and qualifying theidea,up to validating thisideacoming from the startupswiththe communitythe lab community, which is able to challenge the project to give their insights, their suggestions, and then vote.

On everyquarter,anew batch of startups are presenting their projects. They arepitchingusing the platform.Havingas arecord, all of those discussions on the project from several,you knowmentorsexperience givingtheiropinion, the committee'svoting includes our CEO himself, with afewmembers of theboardsvalidating the project based on all these discussions.Soits a very flexible process.A veryrapid process,considering we have a big company.Inless than a few monthswe canorchestrate acompletelynew project.

Its a complete reverse approach than building a PowerPoint document to validate a project. It'savery cool innovationwithinclusivemethodologywhere every volunteer, everyperson,whowants to contributearewelcome to.And obviouslywhen validated,the startups get free access to our software, to those mentorsthat arerecruited.Like, you know, on dating apps, but we are doing matching betweenmentors that haveexpertise and skills withneedsrequested by those startups or on projects.

Laurel:Thats quite a benefit for a startupforpeople be matched with mentors and other innovators in theirparticular field.But to have DassaultsCEO so intimately involved in these processes?That isreally quiteastounding.

Frdric:Its huge.Even if the startup is not selected, we are working on the project, we are challengingthe project with experts. Our CEO himself is challenging the project. It's already important information for those guysand ahuge value.To answeryour question about the themes, we have three main themes that drives our sourcing:life,city infrastructure,andlifestyle well-being. As I said, what we want is to positively impactthe society. We believe that the only progress ishuman. So those themes, as you understood, are driven into a better world.

Laurel:Whats an example of one of these startups that have come to you, what are they working on?

Frdric:We have a huge variety of projects. We have amazing projects that,for instance,thatare performing organ3D printing with patient-specific geometry reconstruction in order to create a virtual twin of a patient. This isin order to haveasimulatorforthesurgeonswho would use it to train before the real surgery in the operating room. It was one of the first startups,BioModex, a French startup. We accelerated at the beginning of the lab. They started at two peopleand arenow at50and they started inParis. Theyhave now also settled inBoston to connect with the life science community. And it ishuge if you look at it, especially for neurosurgery, in some complex case, thesurgeoncan train on your own digital twin before the real surgery. So, itreduces riskwithhigher efficiency.

Another example is about mobilityondrones. We are helping young startupsthat are working ona solarautonomousdrone. You remember,there isastory about solarimpulse with Bertrand Piccard, a pioneer whodid a worldtourwith aplanepowered bythe sun.Thelimit of this project was the pilot,because you cannot staytoolong, not drinking, not eating. I thinka drone disruptscompletelythe concept.This solar autonomous drone is due toperformandoperatemissions, likeforest fire detection.So,ifadronecan stay ontheradar ofany fires early in the process, it would helpcontrollingborders or coasts or pipeline monitoring.Weareworking onit for the pastthree years.Lastsummer, they did their first test flight--12 hourspowered by the sundoing 600 kilometers.So, the first flight was a success and there islotof potentialinthis project.It's calledadrone, but it's more like a plane with two wings.

The third one is a US-based companySparkCharge. They are creating portable, ultra-fast charging units for electrical vehicles. Twoweeks ago, they wereonShark Tank on ABC and they won. They got funded by Mark Cuban.Its a huge success.

Laurel:We should take a minute to define digital twin. A digital twin is a copy of a system that can be manipulated to experiment with different outcomes.Sort of like making a photocopy to preserve the original, but to be able to write on or make changes to the copy.In this case, having a digital twin for a medical procedure helps the surgeon walk through what she is going to do before she does it on a livepatient.

And the second idea of a solar autonomous drone/plane, really, because its not a small drone that we think of, it's a very large one with solar panels on it.Being able to autonomously fly for hours on end to survey forest fires or even oil pipelines, any kind of long flight ability-that really does sound like the future to me. Do you ever just pinch yourself and say, I cant believe these are some of the amazing projects people are coming to us with?

Frdric:Yeah,the future is now. This 3D printed organ is in production andit isalreadybeingused.The solar autonomous dronemade its firstflight,and we expect several flights next year.Thingsare accelerating for the good.

Laurel:And speaking of one of the most important things thatwe aredealing with here in 2020 is thecovid-19pandemic. DassaultSystmeshad a directresponse,as many companies are working very closely with trying to work on solutions to the virus. So,what is theOpenCovid-19project and how's Dassault helping?

Frdric:As I said earlier, the 3DEXPERIENCE Lab has had two kind of projects:a very collectiveandcollaborative project around a startup or a complete community project with special needs. We didthatfor instance, to reconstruct Leonardo da Vincismachinesin 3D.We created an online community,sharedthe collectionall those manuscripts that were the draftings of Leonardo at that timetoengineers.We are using our software, or any3D software, to designandengineer those machines and it workedpretty well. Itstartedeight years ago, andit isstill going. Many machineshavebeen reconstructedand nowthey areforming a playgroundofmany machines. Some of themworked and some of them did not.At that time,heinvented so many things,butobviously not everything wasgoing towork. We did the same for thecovid-19situation.

When the pandemic started,itwas in China, andour colleagueswere reporting the issues to us.Wesaw the pandemic coming intoEurope from Italy first, and then in France. So,we decided to first work with our data intelligence to understandthe needs by developing dashboards to scan what people were saying. And very quickly weidentifiedtwo main needs:ventilatorsandprotection.They were thefocus of things.

Sowe created an open community,OpenCovid-19,to welcome any volunteer makers, engineers anddesigners, to help, because we saw at that timethatmany people were trying to do things, but on their own, in theirlab,in their country. Theywere all facing the same issues, andbyworking together, we thoughtitcouldbe an interesting way to accelerate, to transfer the know-how, to avoid any mistakes done already.

For this community, we acceleratedmore than 150 projectsontheglobal standpoint.With around 25 ventilators in India, a startup calledInalidid a complete engineering simulationandprototyping of a new ventilator in eight days. Onceagain thanks to thecloudand the mentoring.

For collaborative projects with industry, it was the case in Brazil and Mexico.To make these projects you havemakers in the fab lab, trying to do somefrugalinnovation with what they have.Some of those projects havebeencertified,for instance, from whenwe worked with theFabFoundationfrom MITs Center for Bits and Atoms (CBA). They are gathering, with this foundation, all fab labs around the world to connect localproduction.It was mainly the case for protection, for PPE and for faceshields, so that they could 3D print those faceshields.And wewereabletodosome data and GPS localization of those fab labs in hospitals. I thinkurgencydictates to connect them locally so that you can connectto alocal production. A fab lab could develop on designand fabricatePPE forthe healthcare workers close by.

Laurel:And one of those projects that obviously got a lot of interest,is the way that sneeze particles are spread. And withcovid-19, everyone is very interested in understanding how aerosol particles move through the air.

Frdric:Yeah,that istrue. We developed a sneeze simulation model fromthe front of aperson to model virtual particles to see the scientific simulation of the humansneezeto evaluatehowpathogens, suchascovid, wouldspread. And we did this simulation model with MITs CBAwith NeilGershenfeldtofirst announce the design of the PPE, personal protective equipment, the face shielddesign. And to see from two virtualpersonsin front of them,one- or two-metersdistancewhere one is sneezing. What is impact on how those particles would spreadfrom one person to the other,to optimize thedesign?We very quickly understood,for instance, that those face shieldsneeda top coversincethe particles are dropping downandinfecting theotherperson.

Laurel:So how do you see artificial intelligence augmenting human intelligence?

Frdric:AI, for many people, AI is deep learning.It ismachine learning computer vision,or data scienceeverybodyisdoing it. For us,artificial intelligence also leads to generative designs,for instance. The algorithm creates a shape that meets your design intent, your constraints. So,the designer is no longer sketching the shape he wants,he isproviding the constraints. The requirements on the algorithmisproposing a design shape that meets those intents. Itreversescompletely the way the designers performthefunction thanks tothe artificialintelligence.

We spoke abouthuman, augmentedhumanby leveraging the virtual twin. Your virtual me,in a way,of your body,of your organs. We have this collaborative project called Living Heart driven by our American colleagues to revolutionize,thecardiovascularsciencethrough realisticsimulations. This research projectdelivered a heartmodel to explore novel digital therapies. And from this model, we accelerated a new startup, a Belgium company calledFEopsthat now can offer the first and the only patient specific simulation model for structuralheartintervention with AI, which will predict the bestTAVI[those valve implants]that the surgeon would needformatching correctlyhispatientsanatomy.

Laurel:So,the simulation really does come out of the cloud, and out of the computer to real life.And, ina rapid way that helps people on a day-to-day basis, which isreally fantastic. It's not something that just lingers around for approval. You can make changes, see the effect, and then move on to see what else you can do to improve situations.

The face shield project is also one of those thatisso critical.Bringing in the makers, as you said, somany folks wanted to get involved,and still are from around the world,andhelping outin their own way. So,this idea of bringing in amateur makers, as well as startups, as well as these professionals, as well as enterprises, all working together to really combat a global pandemic is reallyquitesomething else. This shows me thatDassault really does have an innovator's mindset when it comes to science, when it comes to helping humanity.Howelse are you seeing the successes of the 3DEXPERIENCE Labsort of ripple throughout the Dassault?

Frdric:At DassaultSystmesyes, we are all innovators in a way.Thats why,when I established this3Dlab initiative five years ago,I decidednotto create a new organization with the boss that would perform innovation. I was willing to have an inclusive management system.Wedecided toallowany of our 20,000 employees to take up to 10% of their time to volunteer on innovation acceleratedby the lab. And bring their hard skillsandtheir know-howknowledge.

And again, this is possible thanks to this platform.Sowe invented, in a way,a new management organization with communities, completelyacrosssilos,acrossdivisions, so that anyone could join a project forfewhours,a few daysora few weeks in order to work on it.Itwas really a new governancefor open innovation, withnew management methodologies that impacted not only the person,orthe employees, but also our own platform on solutions. We work closely with ourR&D to enhanceafew or to develop new applications, to sustain new methodologies on process.

Laurel:And do other companies come to Dassault to ask,how did you do this?Youre a large corporation, with global offices, and youve been around for a long time. You probably have very specific ways of thinking. How did you manage in five years to become this innovative company, they must want to learn from you?

Frdric:Thats true. I don't know if they want to learn from us, butatleast get inspiration from us. What we do is wearealwaystrainingaheadof our time. Thinking ofnew ways of working at the lab. We experimentedwithnew usage,thanks to thecloud. Wesucceededbecause now it really works with 20,000 people in operation with deliverablesandKPIs.Ourpoint is really to inspire themandto show them what is possibleandwhat we can do to transformourselves. It's also digital transformation forDassaultSystmeswiththese employeesin order forthem to think how it could also impact them, how they can also transform their management systemandtheir companies.

Laurel:Thats excellent. What a perfect way to end todays interview. Thank you so much for joining us.

Frdric:Thank you,Laurel.

Laurel:Thatwas Frdric Vacher, the Head of Innovation at DassaultSystmes, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review overlooking the Charles River.

Thats it for this episode of Business Lab. Im your host, Laurel Ruma. Im the director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can find us in print, on the web, and at dozens of events each year around the world and online.

For more information about us and the show, please check out our website at technologyreview.com. This show is available wherever you get your podcasts. If you enjoyed this episode, we hope youll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Collective Next. Thanks for listening.

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Leveraging collective intelligence and AI to benefit society - MIT Technology Review

Cyber criminals could exploit emerging technologies including artificial intelligence and machine learning to help conduct attacks against autonomous cars, drones and Internet of Things-connected vehicles, according to a report from the United Nations, Europol and cybersecurity company Trend Micro.

While AI and machine learning can bring "enormous benefits" to society, the same technologies can also bring a range of threats that can enhance current forms of crime or even lead to the evolution of new malicious activity.

"As AI applications start to make a major real-world impact, it's becoming clear that this will be a fundamental technology for our future," said Irakli Beridze, head of the Centre for AI and Robotics at the United Nations Interregional Crime and Justice Research Institute. "However, just as the benefits to society of AI are very real, so is the threat of malicious use," he added.

SEE:Cybersecurity: Let's get tactical(ZDNet/TechRepublic special feature) |Download the free PDF version(TechRepublic)

In addition to super-powering phishing, malware and ransomware attacks, the paper warns that by abusing machine learning, cyber criminals could conduct attacks that could have an impact on the physical world.

For example, machine learning is being implemented in autonomous vehicles to allow them to recognise the environment around them and obstacles that must be avoided such as pedestrians.

However, these algorithms are still evolving and it's possible that attackers could exploit them for malicious purposes, to aid crime or just to create chaos. For example, AI systems that manage autonomous vehicles and regular vehicle traffic could be manipulated by attackers if they gain access to the networks that control them.

By causing traffic delays perhaps even with the aid of using stolen credit card details to swamp a chosen area with hire cars cyber attackers could provide other criminals with extra time needed to carry out a robbery or other crime, while also getting away from the scene.

The report notes that as the number of automated vehicles on the roads increases, the potential attack surface also increases, so it's imperative that vulnerabilities and issues are considered sooner rather than later.

But it isn't just road vehicles that cyber criminals could exploit by exploiting new technologies and increased connectivity; there's the potential for attackers to abuse machine learning to impact airspace too.

Here, the paper suggests that autonomous drones could be of particular interest to cyber attackers both criminal or nation-state-backed because they have the potential to carry 'interesting' payloads like intellectual property.

Exploiting autonomous drones also provides cyber criminals with a potentially easy route to making money by hijacking delivery drones used by retailers and redirecting them to a new location taking the package and selling it on themselves.

Not only this, but there's the potential that a drone with a single board computer could also be exploited to collect Wi-Fi passwords or breach routers as it goes about its journeys, potentially allowing attackers access to networks and any sensitive data transferred using them.

SEE: 10 tech predictions that could mean huge changes ahead

And the report warns that these are just a handful of the potential issues that can arise from the use of new technology and the ways in which cyber criminals will attempt to exploit them.

"Cybercriminals have always been early adopters of the latest technology and AI is no different. As this report reveals, it is already being used for password guessing, CAPTCHA breaking and voice cloning, and there are many more malicious innovations in the works," said Martin Roesler, head of forward-looking threat research at Trend Micro

One of the reasons the UN, Europol and Trend Micro have released the report is in the hope that it'll be seen by technology companies and manufacturers and that they become aware of the potential dangers they could face and work to solve problems before they become a major issue.

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Artificial intelligence could be used to hack connected cars, drones warn security experts - ZDNet

In some of the most remote and impoverished corners of the world, where respiratory illnesses abound and trained medical professionals fear to tread, diagnosis is increasingly powered by artificial intelligence and the internet.

In less than a minute, a new app on a phone or a computer can scan an X-ray for signs of tuberculosis, Covid-19 and 27 other conditions.

TB, the most deadly infectious disease in the world, claimed nearly 1.4 million lives last year. The app, called qXR, is one of many A.I.-based tools that have emerged over the past few years for screening and diagnosing TB.

The tools offer hope of flagging the disease early and cutting the cost of unnecessary lab tests. Used at large scale, they may also spot emerging clusters of disease.

Among all of the applications of A.I., I think digitally interpreting an image using an algorithm instead of a human radiologist is probably furthest along, said Madhukar Pai, the director of the McGill International TB Center in Montreal.

Artificial intelligence cannot replace clinicians, Dr. Pai and other experts cautioned. But the combination of A.I. and clinical expertise is proving to be powerful.

The machine plus clinician is better than the clinician, and its also better than machine alone, said Dr. Eric Topol, the director of the Scripps Research Translational Institute in San Diego and the author of a book on the use of A.I. in medicine.

In India, where roughly one-quarter of the worlds TB cases occur, an app that can flag the disease in remote locations is urgently needed.

The Chinchpada Christian Hospital in Nandurbar, a small town in northwest India, serves members of the Bhil tribal community, some of whom travel up to 125 miles to visit the center. The 50-bed hospital has eight doctors, and only the most rudimentary medical equipment.

Clear across the country, Simdega, one of the 20 poorest districts in India, is isolated from the nearest town, Rourkela, by nearly five hours of travel on bumpy roads. The tribal population in the district lives in tiny hamlets surrounded by dense, evergreen forest. Simdegas medical center, which has 60 beds and three doctors, is in a clearing of the forest literally in the middle of nowhere, said Dr. George Mathew, the director.

The meager staff has to manage everything that comes its way, from malaria to myocardial infarcts to convulsions to head injuries, Dr. Mathew said. Over the years he has taught himself to read X-rays, and when he is stumped he appeals to the radiologists among his far-flung friends and former colleagues.

Though Nandurbar and Simdega are separated by more than 800 miles, their populations are startlingly similar. Malaria, sickle cell disease and TB run rampant among them, compounded by poverty, reliance on spiritual healers and alcoholism even among the children.

TB tends to get neglected and diagnosis is delayed often, said Dr. Ashita Singh, the chief of medicine at the Nandurbar hospital. By the time people arrive at these medical centers, they often are very, very ill and have never even been evaluated anywhere else, she said.

But in some patients, the X-rays carry signs that are too subtle for a nonexpert to detect. Its in that group of patients where A.I. tech can be of great benefit, Dr. Singh said.

The arrival of the coronavirus and the lockdown that followed cut off these remote hospitals from the nearest towns, and from radiologists, too. It also further delayed and complicated TB diagnoses because both diseases affect the lungs.

A few months ago, both hospitals began using qXR, an app made by the Indian company Qure.ai and subsidized by the Indian government. The app allows the user to scan an X-ray. If it finds evidence of TB, it assigns the patient a risk score. Doctors can then perform confirmatory tests on patients with the highest risk.

At the hospital in Nandurbar, the app helped diagnose TB in 20 patients in October, Dr. Singh said.

Apps like qXR may also be useful in places with a low prevalence of TB, and for routine screening of people with H.I.V., who are at high risk of contracting TB, as well as for those who have other conditions, experts said.

Confused by the terms about coronavirus testing? Let us help:

Most chest X-rays for people who are suspected of having tuberculosis are read by people who are not remotely expert at interpreting them, said Dr. Richard E. Chaisson, a TB expert at Johns Hopkins University. If there were an A.I. package that could read the X-rays and the CT scans for you in some remote emergency room, that would be a huge, huge advance.

qXR is among the more promising of the A.I.-based apps for detecting TB. The company that made the app didnt realize that potential until a doctor at an Indian hospital suggested it a few years ago.

In studies comparing different A.I. applications that were conducted by the Stop TB Partnership, all of the A.I. apps outperformed experienced human readers, and qXR seemed to fare best.

The app identifies TB with an accuracy of 95 percent, according to Qure.ais chief executive, Prashant Warier. But that level of precision is not based on real-world conditions, which Dr. Topol called a common problem with A.I.-based apps. A TB program may be less precise in the United States or Western Europe than in India, because the prevalence of the disease is lower in those places, Dr. Topol added.

The app has only been tested in adults, but it is now being used in children 6 and above. Chest X-rays are particularly useful for pediatric TB because about 70 percent of the cases in children cannot be confirmed by lab tests, said Dr. Silvia S. Chiang, an expert in pediatric TB at Brown University.

There is a huge shortage of trained professionals who feel comfortable interpreting pediatric chest X-rays, she said, so developing and validating computer-assisted X-ray reading technologies in children would greatly help.

Qure.ai said that it was testing its app in children in Bangladesh, and that it would publish the data early next year. In the meantime, qXR and other apps will keep improving because they learn as they go.

The more X-rays you feed the beast, the better it gets, Dr. Pai said.

The experts were optimistic over all that A.I.-based apps could make an enormous impact on the control of TB, especially in countries like India that lack medical resources.

Im just dreaming of a time when something like this would be available to all the little primary and secondary health care centers in the government sector who hesitate to do X-rays because they dont have the confidence to read them, Dr. Singh said. If this was to be made available to every X-ray center in rural India, I think we could beat TB.

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These Algorithms Could End the Scourge of Tuberculosis - The New York Times

In Zach Goldfines view, the fact that it was taking approximately 100 days for veterans to get word that help was coming to process their benefits claims let alone the time to actually receive help was unconscionable.

But that was the reality before the Department of Veterans Affairs launched its Content Classification Predictive Service Application Programming Interface last year. More than 1.5 million claims for disability compensation and benefits were submitted annually, and 65%-80% of them came via mail or fax. And 98.2% of attempts to automate the language in those claims were failing.

A veteran can write whatever, however they think about the injury they suffer. So they might say, My ear hurts and theres a ringing noise constantly but VA doesnt give a benefit for my ear hurts and I have ringing constantly it gives a benefit or gives a monthly payment for hearing loss, said Goldfine, deputy chief technology officer for Benefits at VA. So the problem was that veterans were facing an extra five-day delay in getting the decision on their benefits because there was this back-up of claims at that portion of the process where it required a person to make that translation.

After deploying the API, Goldfine said almost overnight the number of successfully automated claims tripled and it saved VA millions of dollars from reduced time needed to translate claims. This year VA once again reduced wait times with rapidly deployed chatbot to field questions about COVID-19, including what facilities were open.

This was one example of successful artificial intelligence at agencies highlighted during the Impact Summit Series: Artificial Intelligence, presented by the General Services Administrations Office of Technology Transformation Services on Thursday. Goldfine said the API case at VA illustrates how AI can make employees jobs easier, rather than render those employees obsolete a common fear of organizations and managers wary of implementing AI in their offices. Talk to staff and expect to hear different concerns between managers and lower-level workers doing these rote tasks.

We all have parts of our job that we dont like, whether that be like a million emails that we have to respond to or entering certain data elements when we need to take leave there are always monotonous parts of our jobs no matter what our job is and I would guess most folks wish we could automate those away when and where possible, he said. It was manual data reentry in a scenario where, many of them are veterans themselves. Theyd rather be spending their time doing things youd think theyd rather be spending their time doing, like talking with veterans on the phone, understanding whats going on, getting them information they need.

Goldfine also stressed building a multidisciplinary team to implement AI, get buy-in and consider the human impacts. System designers, product managers, user researchers, data scientists, software engineers and even policy experts are all critical.

Alka Patel, head of AI Ethics Policy for DoDs Joint Artificial Intelligence Center, brings a background of engineering and law to her role. She said good engineering principles of design, development, deployment and use are combined with consideration of risk management and government-corporate compliance.

Once DoD adopted its AI ethics principles in February after two years of work leading up to that Patels responsibility was to take those higher-level words and definitions and actually make them tactical.

When it comes to ethical AI, her advice to agencies was to start now and use any existing AI strategy as a framework. And although it may be impossible to predict every scenario or ethical quandary that can arise, some things will probably stay firm, like principles, while testing and evaluations processes are more susceptible to change as technology evolves.

Seeing ethics as an enabler of AI, rather than a hindrance, is the better mindset. In addition, simply stating in an award that contractors must comply with DoD AI ethics principles can help from a signaling standpoint, but Patel was skeptical that it will result in desired objectives.

Im very sensitive to dictating what those requirements need to be done from an agency perspective. I think thats a conversation that needs to happen mutually with our contractors or at least have some insight, she said. We need to be not so prescriptive but we need to be flexible but still have the fidelity of the content and the criteria we are expecting from the contractors themselves.

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Agencies advised to approach AI from an open, collaborative mindset - Federal News Network

Over the lunch-hour din of the cafeteria, there was a shimmer in the aira sense that something great was being discovered.This was different than a normal lunchtime in 1950 at the Los Alamos National Laboratory, where the Cold War had mobilized the Wests brightest minds. Normally, one could expect breakthroughs in particle physics or in fusion power, but there was a buzz on this day that could not be attributed to the Chicken Kiev.

Four great minds were at work solving one of lifes great existential questions: Are we alone in the universe?This question led to more questions, as the Manhattan Project alums scribbled calculations on napkins: How many stars are in the universe?How many are like the Earths sun?How many have planets?How many have planets that are old enough to transmit information as far as the Earth?

It was a frenzy. A crowd began to gather, asmany were aware of the recent reports of UFO sightings nearby. Finally, as the calculations poured forth and the empty Coca-Colabottles piled up, the math becameobvious: There must be intelligent extraterrestrial life somewhere in outer space;the vastness of the universe assured the outcome to be true.

The hand of Enrico Fermi, an Italian-American physicist from the University of Chicago, slammed the tableBAM! The architect of the nuclear age was troubled, though, because the conclusion did not make sense.But where is everybody? he pondered.

The contradiction between the math and the lack of evidenceif the conclusion that intelligent extraterrestrial life exists is so obvious, then where is it?has become known as the Fermi Paradox.

A Computerized Buffett on Every TeamAn Investors Dream

Just as those scientists looked to the stars for signs of intelligent life, investing has for decades looked to computers and quantitative methods for signs of artificial intelligence that can help make smarter decisions. But after decades of experimentation and development, finance is now confronted with a similar paradox.

There is a persistent dream of putting an AI-driven version of Warren Buffet on every investment teamone with all the positive qualities but none of the negative biases and behavioral errors that come pre-installed in humans.The excitement of building such a revolutionary computer-based system to pick investments has driven billions of dollars of investment into developing systems and hiring big-brained PhDs.The share of job openings in finance that are computer or math driven has nearly quadrupled since the Great Financial Crisis.

But despite all the investments made, decades of academic papers produced, computer systems developed, and fortunes made in quant investing, the vast majority of actively managed assets are still non-quantitative in nature.

Traditional active managers will tell you that quantitative techniques are not long-term enough and they will question whether a diverse portfolio can really know anything about the risk of a company.Quantitative practitioners will fire back with a long-datedbacktestor logic derived from (perhaps flawed) statistical techniques, and say, Isnt it obvious that quantitative techniques are superior to anecdotaland heuristic-driven investment?

The two schools of thought are seemingly opposed and have spent the better part of decades without reconciliation. Sure, some quantitative techniques have permeated into risk management or screening for stocks,but there is no AI analyst working side by side with humans to make investment decisions better. Why not?

Combining human-driven investment research with assistance from a junior AI researcher would leverage the best of both worlds.A team like that would combine the long-term, complex thinking of a human with the unbiased, quantitative, evidence-based decision-making of AI.

Combining humans with AI to perform investment research seems such an obvious goal, and the resources being thrown at the problem are vast. But that being so, where are the AI investment analysts? In order to resolve this version of the Fermi Paradox,we need to rethink how finance approaches the use of AI.

The Goal of Embedding AI Has Failed Because the Aim is Misguided

In a classic scene from the movie Jurassic Park, the mathematician Ian Malcolm muses that scientists were so preoccupied with whether or not you could, you didnt stop to think if you should.

This is emblematic of the state of AI research, particularly in its application to quantitative finance.Everyone is so eager to demonstrate they are state of the art that there is no thinking aimed at applying AI in the right way.

The search trends in the graph below demonstrate the fashion for doing something fancy, rather than building something transformative in the right manner.

In quantitative finance, this trend has manifested itself in the overuse (and potentially misuse) of alternative data combined with machine learning.Rather than thinking about the longer-term solutions to the problem, participants in the field are rushing to outperform each otherusing niche data to perform task-specific solutions.

As a result, the alpha itself is fleeting and the applications dont generalize across a broad spectrum of investment problems. Additionally, the industry is laden with tales of good intentions that fail to get adopted into the traditional investment workflow.

Aligning AI with How Investors Think is the Key to Progress

If one stops to think about what makes a great investor, its not typically a niche, task-specific process that differentiates the legends from the temporarily lucky.

Because markets are complex systems whose dancing landscapes are constantly changing, the best investors are generalists by nature; they take mental models and are able to apply them over and over again.They dont merely learn facts; rather, they learn models and systems so as to build a toolkit in order to pick the best tool for the job at hand.

The computational complexity is low and the objective is to handicap all possible outcomesto discount the implied market,not to forecast.They think about what investments present asymmetric payouts from a probabilistic perspective in a folksy back-of-the-envelope manner.

To build AI that can successfully be implemented in the investment process, we must align the design of the machine with the cognitive tasks of great investors.

Our team at UBS Asset Management, called Quantitative Evidence & Data Science, or QED, has taken the approach of focusing on investor workflows as a guiding principle. Essentially,we want to understand what are the things that investors do,so we can betterhelp them make better decisions.

In the next several years, QED will be spending more and more time focusing on how to generalize these workflows and to combine them with heuristics to form investment conclusions.Our goal is to create a form of Artificial General Intelligence (AGI) that can apply reasoning to identify and apply mental models hidden in novel problems and then, ultimately, make an investment recommendation.In the next year, we will focus on aligning our machines with real investment workflows so that the AGI can make real investment recommendations.

This may seem an audacious goal. But the process of getting there is the best way for us to help drive the application of science to the fundamental investment process.As we solve problems in the path towards AGI, we can directly apply the solutions toinvestment workflows.

Finding AI: The Human Plus AGI Analyst Team of the Future

Does this mean that QED is trying to disintermediate human financial analysts?Not at all.In Philip K. Dicks Do Androids Dream of Electric Sheep?which isthe basis for the classic film Bladerunnerhumans apply the Voigt-Kampfftest to potential replicants (AIs) to determine whether they are human or AI.

The test presents disturbing images to the subject: If the subject shows empathy, he/she is human;if no empathy is witnessed,the test proves the subject is AI.Empathy is the secret weapon of human analysts, and because human goalslike saving for retirement, or investing in a climate-aware mannerare the raison detre for investing, we will always need real people in the loop.

While QEDs goal is to developan AGI, it is doing so in the context of having an empathic human working alongside amachine agent to produce better client outcomes.

The benefits of an AI/human partnership to client outcomes are clear and should motivate us to pursue this opportunity. The effort to build a successful integration of AI into the investment process doesnt need to yield inconclusive results like the Fermi Paradox. Finance must align the design of AI with how investors think, and as part of an empathic human partnership. Otherwise, the efforts are in danger of becoming just a fancy tool that operates at the periphery, and well all be left to ponder that, if it was so obvious,then where are all the AI analysts?

Bryan Cross is thehead of UBS Asset Managements Quantitative Evidence and Data Science team (QED). To read more on how QED functions inside of UBS AM, click here. Bryan also joined the Waters Wavelength Podcast to talk about a range of topics in the field of quantitative finance.

Originally posted here:

Fermi's New Paradox: If AI Analysts Are So Obvious, Where is Everybody? - http://www.waterstechnology.com

The results of this years McKinsey global survey, The state of AI in 2020, suggests that organisations are using artificial intelligence (AI) as a tool for generating value.

This years survey found that a small contingent of respondents coming from a variety of industries attribute 20% or more of their organisations earnings before interest and taxes (EBIT) to AI.

McKinsey reported that these companies plan to invest even more in AI in response to the coronavirus pandemic, which suggests that there will be a wider gap between organisations leading in the deployment of AI and the majority of companies that are struggling to capitalise on the technology.

The survey found that the largest shares of respondents report revenue increases for inventory and parts optimisation, pricing and promotion, customer-service analytics, and sales and demand forecasting.

According to McKinsey, the areas of AI deployment that most commonly led to cost decreases are optimisation of talent management, contact centre automation and warehouse automation. Over half of respondents who deployed AI in these areas said they had reduced costs.

According to McKinsey, the organisations that have been pioneers in the use of AI to drive business growth tend to engage in a number of common practices.

In particular, McKinsey found that leaders need to commit more resources to AI initiatives. The study found that AI high performers invest more of their digital budgets in AI than their counterparts and are more likely to increase their AI investments in the next three years.

High performers also tend to have the ability to develop in-house AI-based applications, and generally have a larger workforce of data engineers, data architects and translators than companies that are less advanced in their use of AI.

According to McKinsey, they also are much more likely than others to say their companies have built a standardised end-to-end platform for AI-related data science, data engineering and application development.

Commenting on the findings, Michael Chui, partner at McKinsey Global Institute, said: What weve said in the past about following the money to find where AI adds value in organisations still holds true.

Its also clear that were still in the early days of AI use in business, with less than a quarter of respondents seeing significant bottom-line impact. This isnt surprising achieving impact at scale is still elusive for many companies, not only because of the technical challenges but also because of the organisational changes required.

While organisations in the IT and telecoms sectors have benefited most from the deployment of AI, McKinsey reported that organisations outside of tech also experienced a 20% increase in earnings thanks to AI. It is possible for any company to get a good amount of value from AI if its applied effectively in a repeatable way, Chui added.

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Pioneering companies use AI to drive profit - ComputerWeekly.com

November 19, 2020• Physics 13, 180

Bicycles and indoor lighting are among many everyday features that can benefit from recent advances in artificial intelligence.

In the digital world, the stuff of science fiction has become the fabric of daily life: Our environments tailor themselves to our personal tastes; our handheld devices infer our thoughts; and machines perform many daily tasks at home and at work. Earlier this month at the Future Technologies Conference (FTC) 2020, researchers from around the world came together virtually to discuss the latest computing, engineering, and data science behind artificial intelligence tools. Motivating some of these developments is the growing need for thoughtful and safe sharing of spaces, from cities to workplaces. Representative of this sharing emphasis are two novel concepts: bicycles rigged for data collection and office environments that put everyone in the spotlight.

Bicycles may be tried-and-true technology, but they still have room for innovation. As bicycle sharing becomes a popular solution for city dwellers, some researchers are seeing an opportunity to create decentralized networks of mobile sensors. Several bike-sharing companies have started to equip their vehicles with detectors that collect information about environmental and geospatial conditions, which urban planners can use to improve city infrastructure and other community development projects.

To harness the potential of these new data streams, Andres Rico and his colleagues at the Massachusetts Institute of Technology (MIT) have designed a mobile platform for electric-assist bicycles that incorporates a camera, a GPS module, and multiple environmental sensors. Data collected by each part of the system are combined to give a holistic description of a given bike trip and a better understanding of the riders relationship with the bike and the immediate environment. This, in turn, leads to deeper understanding of the urban surroundings. For instance, it can identify commuter route preferences that may indicate riders perceptions of safety in their community.

Rico and his colleagues have developed software that can uncover more subtle information within bike trip measurements. For example, by examining the location of frequent changes in acceleration they could identify hazardous road conditions, such as potholes or dangerous intersections. The software could also utilize bicycle-provided readings of temperature, humidity, light intensity, and bike speed to pinpoint areas that are adjacent to heavy car traffic, especially high-speed traffic that could pose a danger. The team have met with city officials in Boston and Shibuya, Japan, where, says Rico, there is plenty of interest in supporting further development of the system.

Another aspect of shared living addressed at FTC is lighting. Smart lighting systems respond to voice and gestures to illuminate areas for particular users. However, this green solution can pose a problem in a communal workspace, where different workers may have different lighting needs. To create a more user-friendly space, Elena Kodama and her MIT colleagues developed a dynamic system that senses occupants activity and accordingly generates a separately controlled light for each person in a room.

According to Kodama, previous research on smart shared lighting has mainly focused on energy-saving solutions that turn off lights when no one is around. Her teams approach brings in context awareness, which is a programming strategy that uses data from the environment to form a tailored response. By implementing several advanced control features for programmable light fixtures, the MIT team developed a prototype that compares real-time user position data to the lighting locations. Based on that mapping, commands are sent to adjust the brightness or change the color temperature of the corresponding light fixtures.

Kodamas system can follow up to six users around the room and turn lights out when all the users leave. Hand gestures can change the hue, and the light can be equally split based on the number of users present in the shared space. When two or more users stand close to each other, features of their associated lights blend according to an algorithm that favors the stronger light. As the system becomes context aware, it could eventually learn from each users behavior to anticipate their lighting requirements.

These and other future technologies will continue to transform our daily lives, making our tools smarter not just for our personal use but also for the benefit of the larger community.

Rachel Berkowitz

Rachel Berkowitz is a Corresponding Editor forPhysics based in Vancouver, Canada.

Original post:

AI Tools Boost Simple Technologies in a Shared World - Physics