Daily Archives: November 20, 2020

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.

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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.

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AI Tools Boost Simple Technologies in a Shared World - Physics

Canvas, a company that uses machine learning to install drywall at construction sites, emerged from stealth today. Canvas was founded in 2017 and uses a modified JLG lift, robotic arm, and sensors to automate drywall installation.

Once that task is perfected, Canvas plans to expand into areas like painting and spray-on insulation. The company focuses on commercial construction sites larger than 10,000 square feet, and Canvas founders say its machines operate faster and at a higher level of quality than humans working without a robot.

A lot of our knowledge here comes from working with the U.S. military on surface preparation and finishing and other things like aircraft and ship vehicles, Canvas founder Kevin Albert told VentureBeat in a phone interview.

Whereas some robotics companies sell or rent hardware, Canvas machines are run by trained workers from the International Union of Painters and Allied Trades.

We are for all intents and purposes a tech-enabled subcontractor in our customers eyes, Albert said. Were very excited about the union, and we think thats a great way and a great future for bringing this type of machinery into the world.

Canvas only operates in the San Francisco Bay Area, but it is gearing up to move into other cities. These expansion plans come as the U.S. economy continues to falter due to COVID-19 and mismanagement of the pandemic.

An Associated General Contractors of America survey released earlier this week found declines in major construction projects in large cities across the United States during the pandemic. The survey also found that a majority of firms are expected to cut jobs or freeze hiring in 2021. Conventional construction companies like Caterpillar and Komatsu have also experienced declines in hardware sales this year. But as fewer bulldozers and excavators are sold, companies are turning to AI services for construction, mining, and space.

When asked how Canvas plans to succeed in this environment, Albert said Many months into this crisis we have been growing, and given the type of work we do, we dont expect much of an impact to our growth as things continue.

Canvas has 30 employees and has raised $19 million from investors that include Innovation Endeavors, Obvious Ventures, Brick & Mortar Ventures, and Grit Ventures.

In other compelling robotics news, Walmart recently stopped using Bossa Nova Robotics to scan store shelves and Hyundai reportedly wants to buy Boston Dynamics.

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Canvas emerges from stealth with AI for drywall installation - VentureBeat

It was just two years ago when the Joint Artificial Intelligence Center was created to grab the transformative potential of artificial intelligence technology for the benefit of America's national security, and it has grown substantially from humble beginnings.

Dana Deasy, the Defense Department's chief information officer, and Marine Corps Lt. Gen. Michael Groen, the director of the JAIC, virtually discussed from the Pentagon the growth and goals of JAIC at a FedTalks event during National AI Week.

''One of the things we've wanted to keep in our DNA is this idea that we want to hire a lot of diversity of thought into [JAIC],'' Deasy said, ''but yet do that in a way where that diversity of thought coalesces around a couple of really important themes.''

When JAIC began, it needed to grab hold of some projects that can show people that it can be nimble, agile, and it has the talent to give something that is meaningful back to the Defense Department, he noted.

So JAIC started in a variety of different places, Deasy said. ''But now as we've matured, we really need to focus on what was the core mission for JAIC. And that was, we have to figure out what the role is that AI plays in enabling the warfighter. And I've always said that JAIC should be central to any and all future discussions in that place,'' the CIO said.

''Transformation is our vision,'' Groen said.

''So, it's a big job. We discovered pretty quickly that seeding the environment with lots of small AI projects was not transformational in and of itself. We knew we had to do more. And so, what we're calling JAIC 2.0 is a focused transition in a couple of ways. [For example], we're going to continue to build AI products, because the talent in the JAIC is just superb,'' the JAIC director said.

Groen noted that the JAIC is thinking about solution spaces for a broad base of customers, which really gets it focused.

''There are, you know, the application, and the utilization of AI across the department [that] is very uneven. We have places that are really good. And there, some of the services are just doing fantastic things. And we have some places, large-scale enterprises with fantastic use cases [that] really could use AI, but they don't know where to start. So, we're going to shift from a transformational perspective to start looking at that broad base of customers and enable them,'' he said.

JAIC is going to continue to work with the military services on the cutting edge of AI and AI application, especially in the integration space, where JAIC is bringing together intelligence or intelligence of maneuver, Groen said, ''The warfighting functions have superb stovepipes. But now we need to bring those stovepipes together and integrate them through AI,'' he added.

We have to figure out what the role is that AI plays in enabling the warfighter. And I've always said that JAIC should be central to any and all future discussions in that place.''

The history books of the future will say JAIC was about joint common foundation, Deasy said. ''JAIC could never do all of the AI initiatives with the Department of Defense, nor was it ever created to do that. But what we did say was that people who are going to roll up [their] sleeves, and seriously start trying to leverage AI to help the warfighter every day. at the core of JAIC's success has got to be this joint common foundation,'' he noted.

Deasy noted that the JAIC was powerful and very real.

Into next year, he added, JAIC will have some basic services. And then it's a minimum viable product approach, where JAIC is building some basic services, a lot of native services from cloud providers, but then adding services to that.

''And where we hope to grow the technical platform is a place where people can bring their data, places where we can offer data services, data conditioning, maybe table data labeling and we can start curating data,'' Deasy projected. ''One of the things we'd really like to be able to do for the department is start cataloging and storing algorithms and data. So now we'll have an environment so we can share training data, for example, across programs.''

The modernized software foundation now gives JAIC a platform so it can build AI, Groen said, adding AI has to be a conscious application layer that's applied, leveraging the platform and the things that digital modernization provides.

''But when you think of it that way, holy cow, what a platform to operate from,'' he said.

So now JAIC will really have a have a place where the joint force can effectively operate, he said, adding that the JAIC can now start integrating intel in fires, intel in a maneuver command and control, the logistics enterprise, the combat logistics enterprise and sort of the broad support enterprise, Groen noted.

''You can't do any of that without a platform, and you can't do any of that without those digital modernization tenets,'' the JAIC director said.

If JAIC is going to have the whole force operating at the speed of machines, then it has to start bringing these artificial intelligence applications together into an ecosystem, Groen said, noting that it has to be a trusted ecosystem, meaning "we actually have to know, if we're going to bring data into a capability, we have to know that's good data."

''So how do we build an ecosystem so that we can know the provenance of data, and we can ensure that the algorithms are tested to set in a satisfactory way that we can comfortably and safely integrate data and decision making across warfighting functions,'' the JAIC director asked. ''That's the kind of stuff that I think it's really exciting, because that's the real transformation that we're after.''

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Joint Artificial Intelligence Center Has Substantially Grown To Aid The Warfighter - Department of Defense

Cognite, a global industrial artificial intelligence (AI) software company, and Wood, a global engineering and consulting company, have agreed to a strategic partnership that will accelerate industrial transformation by creating AI solutions that enable more connected, sustainable and data-driven operations for heavy-asset, infrastructure and industrial clients.

The collaboration will deliver value faster and at scale, combining Cognites flagship product, Cognite Data Fusion, with Woods multi-sector domain knowledge, data extraction and technology integration expertise to optimise productivity and performance.

Both companies are committed to deploying performance solutions that address the needs of the energy transition, with the collaboration allowing for greater understanding of existing assets and operations, liberating vast amounts of data trapped in fragmented and legacy systems.

President of Automation and Control at Wood, Mark House, said: Wood and Cognite will leverage physics-based models and AI to quickly provide advanced analytics that drive more profitable and sustainable industrial operations.

Through the partnership, we are addressing a familiar challenge in industry when operational and information technology converge.

John Markus Lervik, CEO of Cognite, said: Working with Wood presents a fantastic opportunity for us to deliver value faster and at scale by playing to each of our strengths. The partnership embraces scalable innovation and value realisation which is accelerated by combining what both Wood and Cognite are best known for in the market.

Through Cognite Data Fusion, data will be transformed from siloed raw information into meaningful digital insights in real-time, to make faster and better-informed business and operational decisions.

Adding Cognites advanced AI data contextualisation and operations product to Woods technology partnership ecosystem is an exciting step as we innovate in connected operations solutions, said Darren Martin, Woods Chief Technology Officer. This collaboration will further enable us to meet the ambitions of our clients and empower them to be future ready now.

Read the article online at: https://www.hydrocarbonengineering.com/refining/20112020/wood-and-cognite-unite-to-unlock-artificial-intelligence-solutions-for-industrial-operations/

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Wood and Cognite unite to unlock artificial intelligence solutions for industrial operations - Hydrocarbon Engineering

After decades of leading the world in sophisticated technological advancements, the United States is now in a precarious position. Our near-peer competitors are catching up, and we can no longer afford to operate in silos, using old formulas that waste precious time and resources. With significant national security consequences on the line, the Department of Defense is embracing enterprise data and decision management to addresses its specific, unique challenges and to make impactful decisions at a moments notice.

The Joint Artificial Intelligence Center is an exemplary group that demonstrates how the Department of Defense needs to work across departments and units as an enterprise. There is no federal agency that faces as many unique business scenarios as the DoD. Its challenges include the highest security requirements, legacy systems, multiple platforms vendors and departments, dark data, interconnected programs, multiyear projections, and fluctuating funding and budget exchanges.

Most government agencies default to spreadsheets, consultants and PowerPoints to make decisions while also using unstructured content documents and data calls to gather enormous columns and rows of information. For the DoD, these decisions are literally foundational and fundamental to operating our armed forces and protecting our citizens from adversaries on physical and digital fronts.

Complex challenges arise from volumes of sensitive information, most of which are siloed due to disparate data sources, insular departmental protocols and legacy systems that cannot keep up. Processes are laden with one-dimensional IT systems, spreadsheets and presentations that require laborious manual edits every time there is a change in budget or a budget drill. Investment decisions are drawn out due to the difficulty of keeping track of authoritative data and the rationale behind decisions, as well as decision parameters at the time of decision.

This is not a criticism but a statement as to the inherent problem of too many sources, platforms and objectives. Its not easy to take non-structured information and make sense of massive amounts of dark data that are not recorded in spreadsheets, such as conversations or the reasons behind decisions found in email, Word documents and correspondence.

The solution is before us: leveraging artificial intelligence and its more nuanced partner, natural language processing. What AI and NLP can do for the DoD is nearly endless, from optimizing existing talent and tracking security threats to synthesizing enterprisewide data for real-time information. What is pivotal is that NLP allows us to integrate dark data, rendering it discoverable and justifiable.

Enterprisewide AI systems create a brain that optimizes data and connections to accomplish the mission and keeps other information in a state of readiness, like nodes and synapses of the nervous system. By leveraging these technologies, entire departments of spreadsheet jockeys or time- and labor-consuming data calls are no longer needed for reporting.

Ingesting the data from multiple sources as well as removing the human factor from data handling and reporting protects it from error corruption. Better still, reports can be generated in moments with real-time data, while still allowing departments the autonomy to manage projects in their existing platforms.

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Traditional approaches are inadequate to process the DoDs massive amount of data and sources. Treating the DoD as an enterprise and leveraging AI and NLP will maximize staff capabilities and make data available, accessible, reliable and secure while providing optimized decisions for budgets, intelligence and actions. Departments can save millions of manhours and dollars so other programs can be funded and staff can utilize the exacting analytics produced.

This solution can also be applied to the budgeting and the program objective memorandum processes. Imagine being able to make precise and specific trade-off analyses to see the effects of budget cuts and their consequences. Solving point solutions around data and automation to get answers, and pulling those applications together, is the formula that the DoD can use to know where programs are connected systemwide. Digital workflows of that information could also assess trade-offs during budget builds, and the real-time data would allow movement within the timeline to inform the current situation and manage decisions.

Our enemies are leveraging technology to work against American defenses. We need to use AI, NLP and other advanced technology to collaborate content and innovate in support of our security and operations. When there is so much innovation and opportunity in the digital stratosphere, matters of national security need every tool at our disposal. Let us not forget the mission is to arm departments and the war fighter with tools for defense, response, communication, action and lethality. Using innovative technology will give our leaders accurate and timely scenarios with a single source of truth and perspective so they have the confidence in the decisions that affect and protect us all.

Dan Naselius is president and chief technology officer at data solutions firm CORAS.

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Unlock AI to transform the Pentagon - C4ISRNet

Date/Time: December 10, 2020 (1-2PM ET / 10-11AM PT)

The challenges facing recycling in the U.S. may seem daunting but cross-sector collaboration is providing a path forward on many of its toughest issues. This kind of collaboration - CPG companies working hand-in-hand with technological innovators, MRF operators and investors - will be critical to solving logjams and current hurdles to improving recycling in the United States. Leaders from AMP Robotics, GFL Environmental, Keurig Dr Pepper and Sidewalk Infrastructures sit down to discuss how their work together is bringing about much needed change to our recycling systems and how this collaborative systems approach proves the power of cross-sector action to address critical issues.

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Speakers:

If you can't tune in live, please register and we will email you a link to access the archived webcast footage and resources, available to you on-demand after the webcast.

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How AI and Robotics are Transforming Recycling | Greenbiz - GreenBiz

All 20 lawmakers are smiling in their official photos. Googles top suggested labels noted a smile for only one of the men, but for seven of the women. The companys AI vision service labeled all 10 of the men as businessperson, often also with official or white collar worker. Only five of the women senators received one or more of those terms. Women also received appearance-related tags, such as skin, hairstyle, and neck, that were not applied to men.

Amazon and Microsofts services appeared to show less obvious bias, although Amazon reported being more than 99 percent sure that two of the 10 women senators were either a girl or kid. It didnt suggest any of the 10 men were minors. Microsofts service identified the gender of all the men, but only eight of the women, calling one a man and not tagging a gender for another.

Google switched off its AI vision services gender detection earlier this year, saying that gender cannot be inferred from a persons appearance. Tracy Frey, managing director of responsible AI at Googles cloud division, says the company continues to work on reducing bias and welcomes outside input. We always strive to be better and continue to collaborate with outside stakeholderslike academic researchersto further our work in this space, she says. Amazon and Microsoft declined to comment; both companies services recognize gender only as binary.

The US-European study was inspired in part by what happened when the researchers fed Googles vision service a striking, award-winning image from Texas showing a Honduran toddler in tears as a US Border Patrol officer detained her mother. Googles AI suggested labels including fun, with a score of 77 percent, higher than the 52 percent score it assigned the label child. WIRED got the same suggestion after uploading the image to Googles service Wednesday.

Schwemmer and his colleagues began playing with Googles service in hopes it could help them measure patterns in how people use images to talk about politics online. What he subsequently helped uncover about gender bias in the image services has convinced him the technology isnt ready to be used by researchers that way, and that companies using such services could suffer unsavory consequences. You could get a completely false image of reality, he says. A company that used a skewed AI service to organize a large photo collection might inadvertently end up obscuring women businesspeople, indexing them instead by their smiles.

When this image won World Press Photo of the Year in 2019 one judge remarked that it showed "violence that is psychological." Google's image algorithms detected "fun."

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When AI Sees a Man, It Thinks 'Official.' A Woman? 'Smile' - WIRED

Cyber-criminals are just getting started with their malicious targeting and abuse of artificial intelligence (AI), according to a new report from Europol and the UN.

Compiled with help from Trend Micro, the Malicious Uses and Abuses of Artificial Intelligence report predicts AI will in the future be used as both attack vector and attack surface.

In effect, that means cyber-criminals are looking for ways to use AI tools in attacks, but also methods via which to compromise or sabotage existing AI systems, like those used in image and voice recognition and malware detection.

The report warned that, while deepfakes are the most talked about malicious use of AI, there are many other use cases which could be under development.

These include machine learning or AI systems designed to produce highly convincing and customized social engineering content at scale, or perhaps to automatically identify the high-value systems and data in a compromised network that should be exfiltrated.

AI-supported ransomware attacks might feature intelligent targeting and evasion, and self-propagation at high speed to cripple victim networks before theyve had a chance to react, the report argued.

By finding blind spots in detection methods, such algorithms can also highlight where attackers can hide safe from discovery.

AI promises the world greater efficiency, automation and autonomy. At a time where the public is getting increasingly concerned about the possible misuse of AI, we have to be transparent about the threats, but also look into the potential benefits from AI technology. said Edvardas ileris, head of Europols Cybercrime Center.

This report will help us not only to anticipate possible malicious uses and abuses of AI, but also to prevent and mitigate those threats proactively. This is how we can unlock the potential AI holds and benefit from the positive use of AI systems.

To that end, the paper highlights multiple areas where industry and law enforcement can come together to pre-empt the risks highlighted earlier. These include the development of AI as a crime-fighting tool and new ways to build resilience into existing AI systems to mitigate the threat of sabotage.

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UN and Europol Warn of Growing AI Cyber-Threat - Infosecurity Magazine