56% of marketers think AI will negatively impact branding in 2020, study says – Marketing Dive

Dive Brief:

As the use of AI expands into a growing array of marketing functions, Bynder'sstudy suggests marketers are concerned with how the technology will impact creativity and branding. Brand building is a top priority for marketers in 2020 following a period when many turned their focus to driving short-term performance lifts.

However, marketers'concerns over automation do not seem to be impacting investments, as most are still ramping up their tech stack and partnerships with martech companies.

"Marketing organizations readily adopted technology for analytics, digital channels and other functions that clearly benefit from automation, said Andrew Hally, SVP of global marketing at Bynder, in a statement. "The challenge ahead is to harness emerging technologies like AI to maintain creative excellence while satisfying business demand for growing volumes and faster delivery."

The Bynder report follows a December study by the Advertising Research Foundation that highlighted how different approaches to data causes tension on marketing teams. That report revealed how researchers and creatives or strategists approach research and data is preventing creative efforts from reaching their full potential. Only 65% of creatives and strategists believe research and data are important for the creative process, while 84% of researchers found it to be key, according to the report. These varying perspectives illustrate that technology can cause issues among marketing teams, despite being foundational to modern day marketing.

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56% of marketers think AI will negatively impact branding in 2020, study says - Marketing Dive

Here are 3 ways AI could help you commute safely after COVID-19 – World Economic Forum

As cities around the world are emerging from lockdowns to stop the spread of COVID-19, public transport companies are facing new challenges. They will have to avoid overcrowding buses and trains to reduce the risk of coronavirus transmission, while ensuring that overall passenger numbers are high enough to sustain the system. Meanwhile, commuters are tentatively returning to public transport, but will only embrace it widely if they see it as a safe, fast and convenient way of reaching their destinations.

Here are three ways cutting-edge technologies such as artificial intelligence can help us all travel at ease, by crunching huge amounts of data, devising optimal schedules and journeys and adapting them to the rapidly evolving situation:

Overcrowding is known to pose a major transmission risk for COVID-19 and other diseases. If countries want to avoid a second wave of infections, one approach is to flatten the typical morning and afternoon peaks in passenger numbers. At least in the medium term, rush hour is likely to be replaced by an even spread of passengers over the course of the day.

Countries around the world are already implementing or preparing for staggered work shifts and school schedules. This helps prevent overcrowding in offices and classrooms, and also spreads out commutes. For public transport companies, this can mean putting on relatively frequent trains and buses all day long, rather than running back-to-back transport during rush hour and more infrequent service during quieter times.

Social distancing and public transport

Image: Optibus

An all-day service has several benefits. It reduces passenger density and facilitates social distancing. It also means drivers are less likely to have to split their shifts and work during busy mornings and afternoons, with sometimes inconvenient off-time in between. Passengers benefit, too, because they can rely on fairly frequent trains and buses all day long.

However, this new model also comes with challenges. Any changes in COVID-19 infection numbers, including local outbreaks and surges, are likely to affect ridership demand. This can happen so quickly that transport providers wont have much time to prepare and adjust. The only way to deal with the uncertainty is to have the flexibility and technological capability to react within days or even hours, and implement the kinds of schedule changes that would have previously taken months of preparation.

This is where artificial intelligence comes in. Transport planning involves vast amounts of data. The number of drivers on duty, the level of passenger demand, the number of available buses and trains, as well as rules such as the maximum hours drivers can work between breaks, and the length of each break, are just some of the many different factors that need to be taken into account.

With the help of algorithms, transit officials can easily create different scenarios based on changes in any of these factors. They can enter changes to the routes and travel times and see the schedule update automatically. They can also handily compare the costs and revenues of the different scenarios. This allows them to respond quickly to broader events that will affect peoples movement, be it lockdowns or staggered shifts. It also means they can quickly put on extra trains and buses if needed.

Mobility systems must be resilient, safe, inclusive, responsive, and sustainable. This is why #WeAllMove, a mobility service match-making platform, launched April 2020 by Wunder Mobility in partnership with the World Economic Forum COVID Action Platform. The platform highlights the importance of leveraging multi-stakeholder collaboration across governments, providers, commuters and more

#WeAllMove consolidates information about a variety of mobility options available in any city, from mode share, to ride share and transit. The independent platform, co-hosted by mobility providers operating globally, will integrate private, public and joint mobility services into a single search and output engine, ensuring a better new mobility normal can be forged, regardless of the crisis ahead.

Since its launch April 2020, it has grown to include 130 mobility service providers offering tailored services in over 300 cities and 40 countries. By bringing public and private stakeholders together, the platform can ensure business continuity for an array of mobility providers, and help secure jobs and services that depend on mobility.

Smart contingency planning

Artificial intelligence (AI) can help us solve seemingly intractable transport problems. Take this example from our own business, which provides advanced technology to public transportation agencies and operators in various countries.

One of our customers wanted to add 5% more trips to their schedule in order to spread passenger numbers over more journeys, and reduce the risk of coronavirus transmission. However, they had 14% fewer drivers to hand. It seemed like an impossible problem. And yet, our AI-driven software found a way of adding more trips with fewer drivers. It did so by optimizing the schedule and extending the average shift by just 45 minutes, while adding in any necessary breaks to adhere to labour and safety regulations. If the driver shortage becomes less severe, transportation providers can easily change their preferences, and the platform would automatically restore the length of the shift.

AI-powered systems also allow operators to quickly provide extra buses to ensure social distancing. Putting on an extra bus may sound simple, but it requires contingency planning, in the form of complex algorithms that can rapidly create alternative scenarios. These enable agencies and operators to figure out which part of the transportation network needs to change to ensure that extra vehicles and drivers are available when needed.

Take a tiny family-owned operator that owns five buses and offers only 50 trips a day. This already results in over 1 billion potential vehicle combinations. Even with a medium-sized transit provider, the numbers become so large that they cant be analysed by humans alone. In big transit-friendly cities like London, some 9,700 buses make 2.2 billion trips a year. Algorithms can sift through all those combinations and choose the optimal solutions within minutes or even seconds.

Image: Optibus

Monitor the impact of altered service

Transport providers have to ensure that all those living along certain routes can still get to their destinations even if schedules are changed at short notice.

With data-driven planning systems, transport officials can enter demographic data from public sources, such as income levels in specific areas. They can then look at a map that overlays the suggested routes with this data. This allows officials to quickly see the potential impact of any changes for residents, including those who may not have the means to use private forms of transport. Such mapping is a fast and simple way of making public transit as inclusive as possible.

All told, there are many different ways social distancing may affect public transit as we encounter the changes that accompany a gradual exit from lockdown. We may not know exactly what transit demand will look like in the coming months, but principles like cooperating with local governments and private institutions to flatten peak times, creating and comparing multiple transit scheduling scenarios, and monitoring the impact that any service changes will have on residents can help transportation providers navigate the unknown roads ahead.

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Here are 3 ways AI could help you commute safely after COVID-19 - World Economic Forum

Everseen lands $10 million so AI can keep an eye on a $45 billion problem – VentureBeat

It is an issue that the retail industry doesnt talk about in great detail, but shrinkage loss of inventory at checkout through non-scans and other errors costs $45.2 billion annually.

Today, Everseen an AI software company founded in Cork, Ireland that detects non-scans at checkout has announced $10 million in funding to help solve this problem through the use of AI and camera technology. The funds will be used to expand operations, including to anewly established New YorkCity office that serves as the headquarters for the companys U.S. operations.

EverseensAItechnology is currently being used by five of the worlds 10 largest retailers. It integrates with security cameras that sit over both staffed registers and self-checkout machines and automatically detects non-scans. When a product is left unscanned, Everseen sends an alert a notification thatincludes an image of the non-scanned item to the retail stores security teams via smartwatch, tablet, or anyothermobile device. Whether it was caused by a simple mistake, a scanning issue (which happens at self checkouts all the time), or an attempt at theft, the alert allows staff to deal with the problem quickly.

That is important, because traditional methods of dealing with shrinkage focus on assessing past activities rather than targeting losses in real time.

Historically, the only way retailers could understand what was happening at checkout was by retroactively data mining their point of sale data and trying to parse through it to create a narrative around their loss and how it influences overall gross margin, Alan OHerlihy, CEO and founder at Everseen, told me. This approach is a waste of time and leaves too much room for error. It also doesnt prevent theft and human error, and definitely doesnt give retailers information that would inform other operational issues that they dont realize track back to error/loss at checkout.

So how is AI powering this application?

Our AI software integrates directly into stores existing cameras and, from day one, understands the DNA of a transaction, meaning that it also knows when its seeing something thatisnta transaction, OHerlihy said. The AI becomes smarter over time, learning the specifics of each individual store its in (spatial layout, shopping patterns, etc.) and being able to identify inconsistencies specific to that stores checkout space.The AI does this by taking theCCTV video data (25 frames per second) and POS Stream Data (time-stamped to the millisecond) and classifying each action and event that occurs at the checkout position. Then our algorithms get to work, detecting nearly everything.

That understanding goes beyond real-time alerts to give retailers actionable data for the future.

Now that retailers have access to POS data thats paired with information about loss incidents, theyre able to determine all the various sources of loss and how they affect their operations as a whole, OHerlihy said.

Of course, AI in retail applications has been dominated by chatbots and smart mirrors. Why has Everseen focused on using AI technology for customer and cashier errors?

I think one of the reasons that chatbots are getting a lot of attention in retail at the moment is because people can relate to them, OHerlihy said. Theyre an accessible, customer-facing application that isnt so far off from other familiar day-to-day interactions they have. Ive seen products that let you walk around a shop and talk to your phone and ask it questions. We think this is cool, but its not going to stop products from walking out of retailers stores.

So while futuristic AI is at the core of the solution, the problem is deeply personal to OHerlihy and is based on traditional retail issues.

I grew up the son of a grocer in Ireland and spent my whole youth working in the family store, so my personal story ties into why Everseen chose to use its AI technology to address theft and customer/cashier error, OHerlihy said. Our store experienced so much loss over the years that we even joke that one of our cashiersbuilt a house with the money they stole. But the methods of tracking the loss were so cumbersome that it just became something you accepted, not something you ever actually solved.

The funds wont just be used for expansion into the US. Everseen will also focus on the future of retail and what can be done to make the consumer experience better, while eliminating shrinkage.

AI and machine learning are the rocket fuel that is enabling us to disrupt the retail industry, OHerlihy said. Our next step is to eliminate physical checkouts altogether, beginning with the introduction of checkout-free shopping (similar to Amazon Go) for major retailers worldwide. We have apatent pending for a virtual manager systemthat will enable our checkout-free shopping technology (0Line), and will make it a reality for retailers. Because were retailer-agnostic, without conflicting consumer interests (like Amazon or Facebook, or Google, for that matter), we believe were in a strong position to lead the retail world into this new phase.

0Line will provide retailers with video cameras and sensors. It will then integrate with inventory, POS, and customer data. That allows the system to identify products as consumers remove items from shelves and to automatically charge them upon leaving the store, along with sending an itemized receipt. It sounds like the perfect retail solution, but it isnt without issues.

The goal is to eliminate legacy technology such as self checkouts, OHerlihy said. It has been a real challenge to integrate our technology with antiquated technology like this.

With the current goal of beating the $45.2 billion shrinkage problem and futureintentions to change retail forever, Everseen is applying AI to retail environments that goes beyond the current crop of chatbots and online-to-offline applications, and thats refreshing to see.

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Everseen lands $10 million so AI can keep an eye on a $45 billion problem - VentureBeat

A European approach to artificial intelligence | Shaping Europes …

The way we approach Artificial Intelligence (AI) will define the world we live in the future. To help building a resilient Europe for the Digital Decade, people and businesses should be able to enjoy the benefits of AI while feeling safe and protected.

The European AI Strategy aims at making the EU a world-class hub for AI and ensuring that AI is human-centric and trustworthy. Such an objective translates into the European approach to excellence and trust through concrete rules and actions.

In April 2021, the Commission presented its AI package, including:

Fostering excellence in AI will strengthen Europes potential to compete globally.

The EU will achieve this by:

The Commission and Member States agreed to boost excellence in AI by joining forces on policy and investments. The 2021 review of the Coordinated Plan on AI outlines a vision to accelerate, act, and align priorities with the current European and global AI landscape and bring AI strategy into action.

Maximising resources and coordinating investments is a critical component of AI excellence. Through the Horizon Europe and Digital Europe programmes, the Commission plans to invest 1 billion per year in AI. It will mobilise additional investments from the private sector and the Member States in order to reach an annual investment volume of 20 billion over the course of the digital decade.

The Recovery and Resilience Facility makes 134 billion available for digital. This will be a game-changer, allowing Europe to amplify its ambitions and become a global leader in developing cutting-edge, trustworthy AI.

Access to high quality data is an essential factor in building high performance, robust AI systems. Initiatives such as the EU Cybersecurity Strategy, the Digital Services Act and the Digital Markets Act, and the Data Governance Act provide the right infrastructure for building such systems.

Building trustworthy AI will create a safe and innovation-friendly environment for users, developers and deployers.

The Commission has proposed 3 inter-related legal initiatives that will contribute to building trustworthy AI:

The Commission aims to address the risks generated by specific uses of AI through a set of complementary, proportionate and flexible rules. These rules will also provide Europe with a leading role in setting the global gold standard.

This framework gives AI developers, deployers and users the clarity they need by intervening only in those cases that existing national and EU legislations do not cover. The legal framework for AI proposes a clear, easy to understand approach, based on four different levels of risk: unacceptable risk, high risk, limited risk, and minimal risk.

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Artificial Intelligence | Carnegie Mellon University – Computer Science …

Balcan Nina Professor ninamf@cs.cmu.edu Conitzer Vincent Professor c@cmu.edu Datta Anupam Associate Professor, CSD, ECE danupam@andrew.cmu.edu Erdmann Michael Professor, Computer Science and Robotics me@cs.cmu.edu Fahlman Scott Research Faculty Emeritus sef@cs.cmu.edu Faloutsos Christos Professor christos@cs.cmu.edu Gupta Anupam Professor anupamg@cs.cmu.edu Hodgins Jessica Allen Newell University Professor of Computer Science And Robotics jkh@cs.cmu.edu Jia Zhihao Assistant Professor zhihaoj2@andrew.cmu.edu Lee Tai-Sing Professor tai@cs.cmu.edu Maxion Roy Research Professor maxion@cs.cmu.edu Mitchell Tom Affiliated Faculty, E. Fredkin University Professor, Faculty Founders University Professor mitchell@cs.cmu.edu O'Toole Matthew Assistant Professor motoole2@andrew.cmu.edu Pollard Nancy Professor nsp@cs.cmu.edu Raghunathan Aditi Assistant Professor aditirag@andrew.cmu.edu Reddy Raj Affiliated Faculty; Courtesy Faculty; Moza Bint Nasser University Professor reddy@cs.cmu.edu Rosenfeld Roni Affiliated Faculty; Department Head, Machine Learning Department; Professor roni@cs.cmu.edu Sandholm Tuomas Angel Jordan University Professor of Computer Science sandholm@cs.cmu.edu Shah Nihar Assistant Professor nihars@cs.cmu.edu Simmons Reid Research Professor reids@cs.cmu.edu Sleator Daniel Professor sleator@cs.cmu.edu Touretzky David Research Professor dst@cs.cmu.edu Woodruff David Professor dwoodruf@cs.cmu.edu

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The Inaugural AI for Good Global Summit Is a Milestone but Must Focus More on Risks – Council on Foreign Relations (blog)

The followingis a guest post by Kyle Evanoff,research associate for International Economics and U.S. Foreign Policy.

Today through Friday, artificial intelligence (AI) experts are meeting with international leaders in Geneva, Switzerland, for the inaugural AI for Good Global Summit. Organized by the International Telecommunications Union (ITU), a UN agency that specializes in information and communication technologies, and the XPRIZE Foundation, a Silicon Valley nonprofit that awards competitive prizes for solutions addressing some of the worlds most difficult problems, the gathering will discuss AI-related issues and promote international dialogue and cooperation on AI innovation.

The summit comes at a critical time and should help increase policymakers awareness of the possibilities and challenges associated with AI. The downside is that it may encourage undue optimism, by giving short shrift to the significant risks that AI poses to international security.

Although many policymakers and citizens are unaware of it, narrow forms of AI are already here. Software programs have long been able to defeat the worlds best chess players, and newer ones are succeeding at less-defined tasks, such as composing music, writing news articles, and diagnosing medical conditions. The rate of progress is surprising even tech leaders, and future developments could bring massive increases in economic growth and human well-being, as well as cause widespread socioeconomic upheaval.

This weeks forum provides a much-needed opportunity to discuss how AI should be governed at the global levela topic that has garnered little attention from multilateral institutions like the United Nations. The draft program promises to educate policymakers on multiple AI issues, from sessions on moonshots to ethics, sustainable living, and poverty reduction, among other topics. Participants will include prominent individuals drawn from multilateral institutions, nongovernmental organizations (NGOs), the private sector, and academia.

This inclusivity is typical of the complex governance models that increasingly define and shape global policymakingwith internet governance being a case in point. Increasingly, NGOs, public-private partnerships, industry codes of conduct, and other flexible arrangements have assumed many of the global governance functions once reserved for intergovernmental organizations. The new partnership between ITU and the XPRIZE Foundation suggests that global governance of AI, although in its infancy, is poised to follow this same model.

For all its strengths, however, this multistakeholder approach could afford private sector organizers excessive agenda-setting power. The XPRIZE Foundation, founded by outspoken techno-optimist Peter Diamandis, promotes technological innovation as a means of creating a more abundant future. The summits mission and agenda hews to this attitude, placing disproportionate emphasis on how AI technologies can overcome problems and too little attention on the question of mitigating risks from those same technologies.

This is worrisome, since the risks of AI are numerous and non-trivial. Unrestrained AI innovation could threaten international stability, global security, and possibly even humanitys survival. And, because many of the pertinent technologies have yet to reach maturity, the risks associated with them have received scant attention on the international stage.

One area in which the risk of AI is obvious is electioneering. Since the epochal June 2016 Brexit referendum, state and nonstate actors with varying motivations have used AI to create and/or distribute propaganda via the internet. An Oxford study found that during the recent French presidential election, the proportion of traffic originating from highly automated Twitter accounts doubled between the first and second rounds of voting. Some even attribute Donald J. Trumps victory over Hillary Clinton in the U.S. presidential election to weaponized artificial intelligence spreading misinformation. Automated propaganda may well call the integrity of future elections into question.

Another major AI risk lies in the development and use of lethal autonomous weapons systems (LAWS). After the release of a 2012 Human Rights Watch report, Losing Humanity: The Case Against Killer Robots, the United Nations began considering including restrictions on LAWS in the Convention on Certain Conventional Weapons (CCW). Meanwhile, both China and the United States have made significant headway with their autonomous weapons programs, in what is quickly escalating into an international arms race. Since autonomous weapons might lower the political cost of conflict, they could make war more commonplace and increase death tolls.

A more distant but possibly greater risk is that of artificial general intelligence (AGI). While current AI programs are designed for specific, narrow purposes, future programs may be able to apply their intelligence to a far broader range of applications, much as humans do. An AGI-capable entity, through recursive self-improvement, could give rise to a superintelligence more capable than any humanone that might prove impossible to control and pose an existential threat to humanity, regardless of the intent of its initial programming. Although the AI doomsday scenario is a common science fiction trope, experts consider it to be a legitimate concern.

Given rapid recent advances in AI and the magnitude of potential risks, the time to begin multilateral discussions on international rules is now. AGI may seem far off, but many experts believe that it could become a reality by 2050. This makes the timeline for AGI similar to that of climate change. The stakes, though, could be much higher. Waiting until a crisis has occurred to act could preclude the possibility of action altogether.

Rather than allocating their limited resources to summits promoting AI innovation (a task for which national governments and the private sector are better suited), multilateral institutions should recognize AIs risks and work to mitigate them. Finalizing the inclusion of LAWS in the CCW would constitute an important milestone in this regard. So too would the formal adoption of AI safety principles such as those established at the Beneficial AI 2017 conference, one of the many artificial intelligence summits occurring outside of traditional global governance channels.

Multilateral institutions should also continue working with nontraditional actors to ensure that AIs benefits outweigh its costs. Complex governance arrangements can provide much-needed resources and serve as stopgaps when necessary. But intergovernmental organizations, as well as the national governments that govern them, should be careful in ceding too much agenda-setting power to private organizations. The primary danger of the AI for Good Global Summit is not that it distorts perceptions of AI risk; it is that Silicon Valley will wield greater influence over AI governance with each successive summit. Since technologists often prioritize innovation over risk mitigation, this could undermine global security.

More important still, policymakers should recognize AIs unprecedented transformative power and take a more proactive approach to addressing new technologies. The greatest risk of all is inaction.

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InveniAI Enters Strategic Artificial Intelligence (AI) and Machine Learning (ML) Focused Collaboration with GlaxoSmithKline Consumer Healthcare -…

GUILFORD, Conn., July 29, 2020 (GLOBE NEWSWIRE) -- InveniAI LLC, a global leader in pioneering the application of artificial intelligence (AI) and machine learning (ML) to transform innovation across drug discovery and development, is pleased to announce a strategic collaboration with GlaxoSmithKline Consumer Healthcare, a science-led global healthcare company with a mission to help people to do more, feel better and live longer, and one of the worlds leading over-the-counter (OTC) medicines company. GlaxoSmithKline Consumer Healthcare will leverage AlphaMeld, an AI and ML platform that empowers internal teams to efficiently evaluate and navigate emerging innovation spanning the Companys key focus areas. The three-year collaboration will grant full access to InveniAIs AI Innovation Lab that encompasses the Consumer Healthcare Module of AlphaMeld and an internal team of cross-functional experts to facilitate accelerated access to innovation.

Michael Keane, Director, Search and Evaluation, GSK Consumer Healthcare, said, At GSK Consumer Healthcare, patients and consumers are central to what we do, and it is their needs that drive innovation and product development at GSK. Our heritage in the pharmaceutical industry ensures that all our products are backed with science and associated data. To unlock and prime innovation, we are always listening and engaging with the consumer to ensure that all our products meet a demand. In doing so, the data that we accumulate is large, broad, and complex. InveniAI has helped us look at tasks that are datacentric across technology road-mapping, search and evaluation, and identifying and understanding new science. AI and ML have helped create efficiencies that shave 4-5 years off the innovation cycle as well as eliminate human biases in identifying innovation.

Aman Kant, InveniAIs Chief Business Officer, said, We are excited about this unique collaboration that will help GSK utilize the power of an AI-driven platform to identify and develop innovative life-changing healthcare products for their consumers. This customized platform delivers tremendous business value by not only rapidly assimilating large volumes of disparate data sets to facilitate a first-mover advantage, but also releases expert human resources at GSK to focus on higher-value tasks. For GSK, the platform enables innovation to be captured as early as patent filing, project funding, or even social media sentiment across several focus areas. He added, We are seeing how this technology can provide industries with a tremendous force-multiplier for rapidly sourcing innovation opportunities.

About GSKGSK - a science-led global healthcare company with a special purpose: to help people do more, feel better, live longer. For further information, please visit http://www.gsk.com.

About InveniAIInveniAI LLC, based in Guilford, Conn., is a global leader pioneering the application of artificial intelligence (AI) and machine learning (ML) to transform innovation across drug discovery and development by identifying and accelerating transformative therapies for diseases with unmet medical needs. The Company leverages AI and ML to harness petabytes of disparate data sets to recognize and unlock value for AI-based drug discovery and development. Numerous industry collaborations in Big Pharma, Specialty Pharma, Biotech, and Consumer Healthcare showcase the value of leveraging our technology to meld human experience and expertise with the power of machines to augment R&D decision-making across all major therapeutic areas. The company leverages the AlphaMeld platform to generate drug candidates for our industry partners and internal drug portfolio. For more information, visit http://www.inveniai.com.

Contact InformationAnita Ganjoo, Ph.D.Corporate Communications InveniAIT: +1 203 273 8388 E: aganjoo@inveniai.com

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How AI is reinventing the onboarding process for HR – TechHQ

Seamless hiring and onboarding is central to stability in an otherwise tumultuous job market. While, like in many other sectors, artificial intelligence (AI) is beginning to prove a boon to Human Resources, some applications of the intelligent, process-expediting technology arent yet watertight.

A recent Sage report titled The changing face of HR consulted organizations on their propensity to adopt the latest tech for HR functions. 43% of respondents believed their firms will not keep up with tech changes over the coming decade. This creates a somewhat troubling outlook.

For starters, overseeing a traditionally (and inherently) human set of functions, HR teams are perhaps prone to lag when it comes to adopting the latest technology. But discussions around AI in HR also conjure images of recruitment bias, where algorithms under the hood of predictive hiring tools have demonstrated bias against African-American-sounding names and female applicants.

According to that report, 24% of quizzed companies are already using AI for talent acquisition (in the form of automation), while 56% claim they will adopt such tech in the coming year.

But there are signs of increasing uptake of technologies in HR, and applications of AI go a lot further in the recruitment and onboarding process than just filtering through thousands of applications.

The stages from interview to offer to negotiation to acceptance and the ease with which you fly through these tells you a whole lot about the business youve just agreed to join. In fact, onboarding is (to many) something of a magic moment, in which new employees decide to stay engaged or become disengaged.

AI steps up to simplify tasks (negating the need for manual document back-and-forths), automating otherwise arduous account setups, and providing feedback on the whole affair to make the next hire smoother. It can track tasks, prompt responses, and even answer questions that may arise from new hires. Here are a few use cases in more detail:

Document generation: Using natural language processing (NLP), organizations can auto-generate offer letters, contracts, and other vital documents with employees. A human still needs to validate the output and ensure that it is signed properly, though.

FAQ chatbots: Most new recruits will have a lot of basic questions (regarding connecting to the office WiFi, setting up an email account, or log-off/screen-lock protocols, among others). A chatbot is a strong way of addressing FAQs whilst retaining a sense of back-and-forth. It can also be continuously tweaked and upgraded as new queries arise.

Networking: Building relationships with peers and team members is crucial for new hires to integrate into an organization, increase productivity, and become engaged employees. Using organizational network analysis (ONA), organizations can understand which relationships new employees must cultivate to be productive, and introduce new hires to critical points of contact in their team and in the organization.

Feedback analysis: Like with literally any process on planet earth (and, Id envisage, beyond), feedback and insight are key to continuous improvement. In order to know how to fine-tune the path through recruitment, AI can provide HR professionals with the tools to understand direct and indirect feedback. Using NLP again, HR managers can extract quality insights from large quantities of textual feedback. This can allow HR managers to gauge themes, employee sentiment, and the overarching effectiveness of HR processes.

Where AI really comes into its own is in its ability to (quickly) adjust what information is required, presented, and completed, based on the specific job in question. For any firm with a sizeable employee base, these nuances can give rise to lag times, inaccuracies, and otherwise poor practice. An intelligent system can give proper permissions, schedule meetings required to understand a role, and even develop tools to help that understanding.

With AI, onboarding doesnt have to happen within regular business hours or at a fixed office location. AI and chatbots can work around the clock, guiding a new hire through all aspects of onboarding and answering questions as they arise. With HR teams busier than ever in coordinating and responding to remote working issues, the onboarding process is one space where tech can really come in handy, allowing new hires to integrate more quickly [] even before their first day on the job, says Susan Power, founder and CEO of Power HR.

Gamification can be one way to utilize AI and set apart your onboarding procedure in one fell, albeit intricate swoop. Adding competitive, enjoyable, game-like elements to the onboarding process can make it easier for human minds to absorb and retain information. AI can help this customized experience both in the recruitment process (with cognitive ability/competency tests) and afterward.

Covid-19 has disrupted many companies typical routine when it comes to onboarding employees. Cognitive automation tools can simplify the process for new hires that may start remotely, and lessen the alienation that can come from limited face-to-face training time.

The crux of the matter is not actually a crux at all, but rather an ongoing navigation of the intersection between human involvement and AI efficiencies. This will indeed change from company to company. The fact is that you cant take the human out of Human Resources. People will remain integral to a robust, personable HR strategy. You can, however, add AI technology at intelligent onboarding touchpoints, diverting human time away from inane clerical tasks to forming real bonds with candidates and growing teams in the most positive of ways.

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How AI is reinventing the onboarding process for HR - TechHQ

Enlisting AI in our war on coronavirus: Potential and pitfalls | TheHill – The Hill

Given the outsized hold Artificial Intelligence (AI) technology has acquired on public imagination of late, it comes as no surprise that many are wondering whatAI can do for the public health crisis wrought by the COVID-19 coronavirus.

A casual search of AI and COVID-19 already returns a plethora of news stories, many of them speculative. While AI technology is not ready to help with the magical discovery of a new vaccine, there are important ways it can assist in this fight.

Controlling epidemics is, in large part, based on laborious contact tracing and using that information to predict the spread. We live in a time in which we constantly leave digital footprints through our daily life and interactions. These massive troves of data can be analyzed with AI technologies for detection, contact tracing and to find infection clusters, spread patterns and identify high-risk patients.

There is some evidence that AI techniques analyzing news feeds and social media data were not too far behind humans in originally detecting the COVID-19 outbreak in Wuhan. China seems not only to have used existing digital traces but also enforced additional ones; for example, citizens in Nanjing are required to register their presence in subway trains and many shops by scanning QR codes with their cellphones. Singapore, lauded for its effective containment of the virus without widespread lockdowns, has used public cameras to trace the interaction patterns of the infected, and even introduced a crowd-sourced app for voluntary contact tracing. In the U.S., research efforts are underway to mine body temperature and heart-rate data from wearables for early detection of COVID-19 infection.

It is widely feared that COVID-19 cases, at their peak, will overwhelm medical infrastructure in many cities. Evidence from Hubei, China, and Lombardy, Italy, do indeed support this fear. One way to alleviate this situation is to adopt novel methods of remotely providing medical help. The basic infrastructure for telemedicine has existed for a long time but has been a hard-sell from consumer, provider and regulatory points of view until now. Already, the U.S. has waived regulations to allow doctors to practice across state boundaries; the Department of Health and Human Services (HHS) has also announced that it will not levy penalties on medical providers using certain virtual communication tools, such as Skype and FaceTime, to connect with patients.

AI technologies certainly can help as a force-multiplier here, as front-line medical decision support tools for patient-provider matching, triage and even in faster diagnosis. For example, the Chinese company Alibaba claims rapid diagnostic image analytics for chest CT scans; China also has leveraged robots in disinfection of public spaces. Remote tele-presence robots increasingly could be leveraged to bring virtual movement and solace to people in forced medical quarantines.

AI technologies already have been enablers of, and defenders against, fake news. In the context of this pandemic, our incomplete knowledge coupled with angst has led to an infodemic of unreliable/fake information about coping with the outbreak, often spread by well-meaning (if gullible) people. AI technologies certainly can be of help here, both in flagging stories of questionable lineage and pointing to more trusted information sources.

AI also can be used to distill COVID-19-related information. A prominent example here is a White House Office of Science and Technology Policy-supported effort to use natural language-processing technologies to mine the stream of research papers relevant to the COVID-19 virus, with the aim of helping scientists quickly gain insight and spot trends within the research. There is some hope that such distillation can help in vaccine discovery efforts, too.

Suppression by social distancing has emerged as the most promising way to stem the tide of infection. It is clear, however, that social distancing like sticking to a healthy diet runs very much counter to our natural impulses. Short of draconian state enforcement, what can we do to increase the chances that people follow the best practices? One way AI can help here is via micro-targeted behavioral nudges. Like it or not, AI technologies already harvest vast troves of user profiles via our digital footprints and weaponize those for targeted ads. The same technologies can be readily rejiggered for subliminal micro-targeted social distancing messages that could include distracting us from cabin fever. There already is some evidence that mild nudging can even reduce the sharing of misinformation.

Lockdowns and social distancing measures are affecting the education of millions of schoolchildren. Tutoring services assisted by AI technologies can help significantly when students are stuck at home. China reportedly has relied on the help of online AI-based tutoring companies such as Squirrel AI to engage some of its millions of schoolchildren in lockdown.

And while self-driving cars remain a distant dream, delivering essential goods to people via deserted streets certainly could be within reach. Depending on how long shelter-at-home continues, we might rely increasingly on such technologies to transport critical personnel and goods.

Some of these potential uses of AI are controversial, as they infringe on privacy and civil liberties or reflect the very type of applications that the AI ethics community has resisted. Do we really want our personal AI assistants to start nudging us subliminally? Should we support increased cellphone tracking for infection control? It will be interesting to see to what extent society is willing to adopt them.

Indeed, our readiness to try almost anything to fight this unprecedented viral war is opening an inadvertent window into how we might handle the worries surrounding an AI-enabled future. Ideas such as universal basic income (UBI) in the presence of widespread technological unemployment, or concerns about diminished privacy thanks to widespread AI-based surveillance all are coming to the fore.

China has mobilized state resources to feed its quarantined population and used extensive cellphone tracking to analyze the spread of the virus. Israel is reportedly using cellphone tracking to ensure quarantines, as did Taiwan. The U.S. is considering UBI-like ideas e.g., providing thousand-dollar checks to many adults effectively unemployed during the pandemic and is reportedly mulling cellphone-based tracking to get people to follow social distancing guidelines.

Once such practices are adopted, they will no longer just be theoretical constructs. Some or all of them will become part of our society beyond this war on the virus, just as many Great Depression-era programs became part of our social fabric. The possibility that our choices in this time of crisis can change our society in crucial ways is raising alarms and calls for circumspection. Yet, to what extent civil society is likely to pause for circumspection at the height of this execution imperative remains to be seen.

Subbarao Kambhampati, PhD, is a professor of computer science at Arizona State University and chief AI officer for AI Foundation, which focuses on the responsible development of AI technologies. He served as president and is now past-president of the Association for the Advancement of Artificial Intelligence and was a founding board member of Partnership on AI. He can be followed on Twitter@rao2z.

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DeepMind AI teaches itself about the world by watching videos – New Scientist

Lions roar: video clips beat labels for AIs seeking knowledge

OE KLAMAR/AFP/Gettyvide

By Matt Reynolds

To an untrained AI, the world is a blur of confusing data streams. Most humans have no problem making sense of the sights and sounds around them, but algorithms tend only to acquire this skill if those sights and sounds are explicitly labelled for them.

Now DeepMind has developed an AI that teaches itself to recognise a range of visual and audio concepts just by watching tiny snippets of video. This AI can grasp the concept of lawn mowing or tickling, for example, but it hasnt been taught the words to describe what its hearing or seeing.

We want to build machines that continuously learn about their environment in an autonomous manner, says Pulkit Agrawal at the University of California, Berkeley. Agrawal, who wasnt involved with the work, says this project takes us closer to the goal of creating AI that can teach itself by watching and listening to the world around it.

Most computer vision algorithms need to be fed lots of labelled images so it can tell different objects apart. Show an algorithm thousands of cat photos labelled cat and soon enough itll learn to recognise cats even in images it hasnt seen before.

But this way of teaching algorithms called supervised learning is cheating, says Relja Arandjelovi who led the project at DeepMind. Instead of relying on human-labelled datasets, his algorithm learns to recognise images and sounds by matching up what it sees with what it hears.

Humans are particularly good at this kind of learning , says Paolo Favaro at the University of Bern in Switzerland. We dont have somebody following us around and telling us what everything is, he says.

Arandjelovi created his algorithm by starting with two networks one that specialised in recognising images and another that did a similar job with audio. He showed the image recognition network stills taken from short videos while the audio recognition network was trained on 1-second audio clips taken from the same point in each video.

A third network compared still images with audio clips to learn which sounds corresponded with which sights in the videos. In all, the system was trained on 60 million still-audio pairs taken from 400,000 videos.

The algorithm learned to recognise audio and visual concepts, including crowds, tap dancing and water, without ever seeing a specific label for a single concept. When shown a photo of someone clapping, for example, most of the time it knew which sound was associated with that image.

This kind of co-learning approach could be extended to include senses other than sight and hearing, says Agarwal. Learning visual and touch features simultaneously can, for example, enable the agent to search for objects in the dark and learn about material properties such as friction, he says.

DeepMind will present the study at the International Conference on Computer Vision which takes place in Venice, Italy, in late October.

While the AI in the DeepMind project doesnt interact with the real world, Agarwal says that perfecting self-supervised learning will eventually let us create AI that can operate in the real world and learn from what it sees and hears.

But until we reach that point, self-supervised learning might be a good way of training image and audio recognition algorithms without input from vast amounts of human-labelled data. The DeepMind algorithm can correctly categorise an audio clip nearly 80 per cent of the time, making it better at audio-recognition than many algorithms trained on labelled data.

Such promising results suggest that similar algorithms might be able to learn something by crunching through huge unlabelled datasets like YouTubes millions of online videos. Most of the data in the world is unlabelled and therefore it makes sense to develop systems that can learn from unlabelled data, Agrawal says.

Journal reference: arxiv.org

Read more: Curious AI learns by exploring game worlds and making mistakes

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Watch Out: You’re in Ai Weiwei’s Surveillance Zone – The New York … – New York Times


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Not just humans, AI too is getting used to the new normal – Gadgets Now

Artificial intelligence (AI) solutions are built using lots and lots of data, most of it historical. When hundreds of pictures of cats are shown to an AI system, it learns what a cat is and what isnt a cat. AI systems in retail learn from historical data what are, for instance, the buying patterns in one store, how it changes over months/seasons, how it is different in another store in another location. That enables it to make predictions.

When something like Covid-19 happens and changes many historical patterns dramatically, AI systems are as unprepared as humans. And they require fresh training, rewriting of the algorithms.

Bengaluru-based AI solutions provider Manthan works with many global retailers. One of the companys fashion retail customers in the US saw almost all its 700 stores shutting down at the end of March. The companys chief product officer Sameer Narula says they had to rework their algorithms to handle the short-term and longer-term impact of the event. For the short-term, we had to quickly build new models, within weeks, which could use e-commerce data to identify changing patterns and trends and mash it with geo-spatial data to predict the demand in stores when they reopened, he says.

The pandemic event-specific data is still limited and evolving to be useful in calibrating the longer-term impact of it

Sameer Narula, chief product officer, Manthan

For the longer term, its more difficult. Narula says the pandemic event-specific data is still limited and evolving to be useful in calibrating the longer-term impact of it. Weve been working on incorporating several new micro and macro economic signals like consumer sentiment index, economic optimism index into our demand forecasting models to bring in more permanent and generic adaptability to deal with the impact of such kind of exceptional events in future, he says.

We used AI to check for compliance of mask usage by delivery partners, and to stop delivery of non-essential items

Dale Vaz, head of engineering and data science, Swiggy

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An AI affair fuels a midlife crisis in the eerie science fiction drama Auggie – The Verge

If a time-traveler from just 15 years ago leaped into our present, theyd probably be baffled by a world where its socially acceptable to wander around in public, continually staring at glowing rectangular screens. Thats how retiree Felix (Richard Kind) feels in the science fiction movie Auggie as he rounds a corner at the grocery store and sees a middle-aged woman in high-tech glasses, chatting away to a virtual assistant that only she can see.

Felix knows just enough about the Auggie virtual companion program to have some frame of reference for whats going on as she playfully swats the air, teasing her Auggie for encouraging her to indulge in a pint of ice cream. But its still a weird, comical sight until Felix tries on his own Auggie glasses. Suddenly, chatting away with a virtual confidante is the most natural thing in the world. How did he ever live without her?

Though on the surface, Auggie seems to share a lot of DNA with Spike Jonzes AI love story Her, it bends that DNA toward a different aim. While Her optimistically explored the idea of how AI might develop sentience and build relationships, Auggie never questions whether Felixs virtual assistant is more than a program. Director Matt Kane and his co-writer Marc Underhill are more interested in what technology has to say about human nature than in how technology itself might realistically evolve. That makes Auggie feel a bit like a feature-length episode of Black Mirror, both for better and for worse. It doesnt have enough substance to fill its runtime, but it explores some intriguingly thorny ideas along the way.

Felix is gifted his pair of Auggie glasses at his retirement party, although hes initially too bitter about being forced out of his company to pay them much attention. Later, however, he watches a glossy online tutorial and learns that Auggie analyzes its users subconscious desires, then projects the image of whatever they want whether thats a personal assistant, a friend, or something more. (Kane absolutely nails the tech worlds aspirational advertisements and sleek packaging.) When Felix slips on his glasses for the first time, he sees a beautiful, doe-eyed, eager-to-please young woman named Auggie (Christen Harper) whos pointedly much closer in age to Felixs 20-something daughter Grace (Simone Policano) than to him or his wife Anne (Susan Blackwell).

At first, Felix is surprised and a bit embarrassed by what his Auggies appearance suggests about him. She sits somewhere between an Instagram model, a cam girl, and a phone sex operator. On the other hand, since no one can see Felixs Auggie but him, she represents a low-stakes way to carry out the beginnings of an affair from the comfort of his own home. Kane films their conversations in dreamy point-of-view shots that emphasize the intimate appeal of the Auggie program, which provides an intimate, dedicated companion. But Kane also punctures the illusion with wide shots that remind us that, like that woman in the grocery store, Felix is ultimately just alone in a room talking to himself. (There are some niggling logistical questions about how Felix can hear Auggie, but theyre mostly easy to ignore since the simplicity of the glasses-based technology is so visually appealing.)

In many ways, Auggie is a familiar midlife crisis story dressed up with some light science fiction elements. With Felixs wife flourishing in her career, his grown-up daughter moving in with her boyfriend, and no job to fill his days, he boosts his ego in the virtual arms of someone young and beautiful. But Auggie also brushes up against bigger questions about how technology isolates people even as it sells the illusion of connectedness. The Auggie system is a flexible metaphor that speaks to everything from the fantasy of porn to the appeal of carefully curated social media feeds. When Felix starts rejecting real-life social events to spend time with Auggie, it doesnt feel too far removed from someone who cancels plans only to spend the evening scrolling through Twitter or goes out for coffee with a friend but spends the whole time looking at their phone.

At its best, Auggie taps into some appropriately chilling ideas about the pitfalls of technology. When Anne tries on a pair of Auggie glasses, she makes the cringe-worthy mistake of describing what shes seeing, not realizing its a projection of her specific subconscious desires, rather than a preprogrammed image. A scene where Felixs Auggie encourages him to take their relationship to the next level raises fascinating questions about the intersection of technology and capitalism. Is she actually catering to his cravings or just pushing the companys latest product, a pair of stimulating underwear called Auggie Touch?

Unfortunately, as the film goes on, its clear Kane is more interested in exploring the Auggie-as-an-affair metaphor than in unspooling the ethical and technological questions the premise raises. Felixs life starts to implode in ways that have cropped up on-screen countless times before: he grows distant from his loved ones and misses out on important events because he lost track of time while with his new girlfriend. Even at a brisk 81 minutes in length, the film is stretching for time by its third act. Auggie probably wouldve worked just as well, if not better, as a short film.

Still, even where it doesnt live up to its full potential, Auggie has some appreciably pointed things to say about heterosexual masculine desires. Richard Kind is brilliant casting for Felix precisely because hes such an amiable presence. For the most part, Felix seems to be a genuinely good guy, which only makes his secret desire for a docile sex object more unsettling. Young, beautiful, and brainless, Auggie has none of the burdensome opinions, feelings, boundaries, or agency of a real-life woman. Though Felix ultimately learns that virtual perfection isnt all its cracked up to be, Auggie raises chilling, perpetually timely questions about what men really want from women.

Auggie will be released simultaneously in select theaters and on VOD on September 20th, 2019.

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An AI affair fuels a midlife crisis in the eerie science fiction drama Auggie - The Verge

One Partner’s Experience Using AI To Measure Customer Satisfaction – CRN: The Biggest Tech News For Partners And The IT Channel

Cloudbakers, a system integrator focused on supporting cloud services and migration for its 500 clients, began measuring customer satisfaction using artificial intelligence a few years ago and has since successfully identified patterns and smoothed out problem areas.

"We see our job as 50 percent human, 50 percent tech. For us, monitoring our customer's happiness is a critical success factor," Eric Lannert, CTO of Chicago-based Cloudbakers, told MSPs at The Channel Company's NexGen 2019 conference.

Solution providers can harness AI technologies like sentiment analysis gain deeper insights into topics that are trending, and positive and negative feedback from their customers, Lannert said.

[Related: AI Explosion: How Solution Providers Are Cashing In Big]

Cloudbakers is a Google Cloud Premier Partner, so the firm selected Google's machine learning natural language API to automatically compute sentiment analysis for its cloud licenses it resells. The industry average response rate to satisfaction surveys typically runs around 30 percent, but CloudBakers wanted to see what would happen if they used AI APIs to innovate on the data it was collecting from its ticket system.

Google's sentiment analysis gave Cloudbakers a quantitative, emotional scoring on a 1-10 basis of text from their customers through the tickets by recognizing specific words and phrases.

"As a reseller, provisioning is usually a sticking point for customers, and we learned that amazingly, that was the most positive with our customers," Lannert said. "Interestingly from a problem-solving standpoint, PDFs were having an issue, which turned out to be a common theme across clients dealing with cloud syncing. The technology is helping us diagnose customer patterns across many different clients."

Today, Cloudbakers is considering reselling AI to track satisfaction as an offering for its clients, rather than only continuing to use it in-house, Lannert said. The firm is talking to its customers to learn which insights they'd pay more or less for, and how much analysis will the customer expect of Cloudbakers.

"This is giving us a good idea to build a pretty big offering set, he said.

Vistem Solutions, Inc. (VSI), an Irvine, Calif.-based MSP, specializes in supporting hospitality and labor union customers, as well as the shipping industry on the Pacific Coast. VSI has its own development team, so the firm can do data integration and software modifications to help disparate systems communicate with each other. VSI is interested in using AI to measure customer satisfaction for its hospitality clients to use during their guests' transactions, not after their experience is over, said Keith Nelson, vice president of technology for VSI.

"Customers like convention centers are very big into guest satisfaction, so I'm interested in tracking satisfaction and surveying along the process, versus at the end," he said. "Having a customer say they're mad afterward doesn't accomplish much."

VSI is in the process of evaluating several new cloud partners for development, including Amazon Web Services and Microsoft Azure, Nelson said.

MSPs interested in offering an AI-based solution to track satisfaction should take advantage of cloud platforms for building scripts that allow these solutions to be deployed in one click and automatically billed, lannert said. Channel partners will also have to identify the right customers within their base that will be open evolving their businesses, he added.

"Your potential early adoptions are people who are interested in machine learning," he said. "This is an example of moving from the procurement of hardware to the procurement of solutions, with the same level of automation and customer experience surrounding it."

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One Partner's Experience Using AI To Measure Customer Satisfaction - CRN: The Biggest Tech News For Partners And The IT Channel

European Patent Office Rejects Worlds First AI Inventor – Forbes

The European patent authorities have rejected an attempt to register an AI as an official inventor.

The possibility's been a subject of debate for some time, and last summer a group of legal experts decided to force the issue. The group, led by Professor Ryan Abbott of the University of Surrey, submitted designs developed by an AI to the authorities in the US, UK and Europe, and later Germany, Israel, Taiwan and China.

The AI concerned, named Dabus, was created by Stephen Thaler, and is described as a connectionist artificial intelligence.

According to its inventors, it 'relies upon a system of many neural networks generating new ideas by altering their interconnections. A second system of neural networks detects critical consequences of these potential ideas and reinforces them based upon predicted novelty and salience.'

It came up with two concepts submitted for patent approval: a new type of drinks container based on fractal geometry, and a device based on a flickering light for attracting attention during search and rescue operations.

"In these applications, the AI has functionally fulfilled the conceptual act that forms the basis for inventorship. There would be no question the AI was the only inventor if it was a natural person," said Abbott.

"The right approach is for the AI to be listed as the inventor and for the AIs owner to be the assignee or owner of its patents. This will reward innovative activities and keep the patent system focused on promoting invention by encouraging the development of inventive AI, rather than on creating obstacles."

However, the European Patent Office failed to agree.

"After hearing the arguments of the applicant in non-public oral proceedings on 25 November the EPO refused EP 18 275 163 and EP 18 275 174 on the grounds that they do not meet the requirement of the EPC that an inventor designated in the application has to be a human being, not a machine," it concluded.

Legal attitudes to AI inventors vary subtly around the world. In the UK, for example, the programmer who came up with the AI is the inventor; in the US, it's the person who came up with the original idea for the invention, with the programmer deemed simply to be facilitating it.

Patent ownership also involves certain responsibilities that an AI would struggle to satisfy, such as renewing patents, updating government records and keeping licensees informed.

While the EU did at one time consider adding 'electronic personality' to the two categories of potential patent owner allowed - 'natural person' and 'legal entity' - it abandoned the idea after receiving a strongly worded letter from more than 150 experts in AI, robotics, IP and ethics.

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European Patent Office Rejects Worlds First AI Inventor - Forbes

Cooper, the grocery assistant with AI, gives concierge service – Mail and Guardian

Swedish supermarket Coop Sweden has a retail grocery assistant on its websites. Cooper, as the assistant is called, can help you with dietary requirements, suggest recipes and provide nutritional information. The idea behind Cooper is to increase interaction with consumers while providing a seamless shopping experience.

As consumers move online, implementing the technologies of the fourth industrial revolution (4IR) is becoming more crucial. Cooper is an example of the 4IR in practice. These technologies are changing the way we work, commute, communicate and, as Cooper will tell you, even shop. The 4IR is based on high-level technology such as artificial intelligence, automation, biotechnology, nanotechnology and communication technologies that permeates society. It is a combination of various technologies that can communicate with humans and interact with other devices and programs.

The lockdown necessitated by the Covid-19 pandemic has been an important yardstick for understanding behavioural changes in consumers as online options become more commonplace. A recent Nielsen study found that 37% of South Africans say they are shopping more online in this period. As Gareth Paterson, a lead retail analyst at Nielsen South Africa, put it, Amid the strange new world of Covid-19, online grocery shopping has been a lifeline for many South African consumers who have desperately sought out safe and secure shopping alternatives amidst the uncertainty of lockdown living. As a result, available online shopping platforms, especially for groceries, medicines, and other necessary items, have seen a surge in usage over the last few weeks as consumers prefer not to venture into stores and have increasingly opted for these reduced touchpoint alternatives.

According to data from the survey, Nielsen is anticipating that options such as click and collect and online personal shopping will grow exponentially, resulting in prolonged behavioural changes. Retailers have been quick to cotton on to this shift and have responded in innovative and effective ways. For instance, Checkers has launched an app called Checkers Sixty60, which has groceries delivered to you in 60 minutes. There are 5 000 groceries to choose from and options to substitute products if your first choice is not available.

In various industries, the coronavirus has been an important lesson where we are well equipped to deal with the 4IR and where we still have gaps. This will undoubtedly signal a shift in consumer behaviour and many will not return to traditional brick-and-mortar retail. We will increasingly see more retailers adapt to this way of operating. In fact, a report by global management consultancy Accenture last year suggested that South African retailers would see a knock to a business if they did not embrace e-commerce. The emphasis on traditional stores, Accenture argues, means that many retailers are losing out on the potential profits that come with online offerings. Yet, interestingly enough, the current pandemic may subvert this.

This is not to say that online shopping has not had somewhat of a watershed moment in recent years. Perhaps the best example that provides a holistic user experience is the Mr Price app. With it, you can shop online, find the stock in stores and even upload a picture of something you like for it to suggest similar items available on the app through the snap and shop feature. For instance, I could either take or upload a picture of a pair of brown formal shoes that I saw a colleague wear. The app will then pull any stock available at Mr Price that looks similar and provide a list of suggestions accompanied by pictures.

The starkest instance of the popularity of online retail is Black Friday, which has gained popularity in South Africa in the last few years. It is probably the biggest day of the year for retailers, particularly online retailers. In the week leading up to it, consumers receive hordes of massive Black Friday discounts. Some of them may have put together wish lists to check out at the stroke of midnight while others may have used their phones to search for discounts.

AI is tailoring the online experience and it is determining prices, inventory and making distribution far more efficient for your favourite retailers. Another example of this on Instagram is the move to online shopping with a new AR shopping feature that is being rolled out, which allows consumers to try on products digitally before buying them. For example, using your phone you could try on the latest shade of Mac lipstick to see how you would look. This followed a rollout of a checkout feature that allowed you to buy products directly on Instagram without ever leaving the app.

The try-on feature is limited to certain brands and is still in a trial phase, but it is as easy to use as the filters when you create a story that could give you dog ears and a tongue or freckles and blue eyes. The long-term vision is to roll this out with all retail, so, for example, you could see what a couch looks like in your living room. This is not the only technology Instagram has adopted. AI influencers have been introduced, which have been surprisingly popular.

According to consumer insight website LendEDU, three years ago 52.9% of millennials said Instagram has the most influence on them when making shopping decisions. For instance, many followers use the website LIKEtoKNOW.it, which sends a direct link to a product after a shopper likes a post. Creating completely digital influencers is a whole new avenue. Miquela is an AI influencer with 2.4-million followers. Just like any other influencer, her posts are perfectly planned, she has a themed feed, has sponsored content and gives her followers useful advice and brand recommendations. But she does not actually exist she is run with AI technology. This has not stopped her career from taking off.

Last year, she collaborated with Prada for Milan Fashion Week by posting 3D-generated gifs of herself at the Milan show venue wearing the spring/summer 2018 collection. On Pradas Instagram account, she gave their followers a mini-tour of the space, just like any influencer would for a brand. She is not an outlier there are many more like her. Balmain recently announced a Balmain Army made up entirely of computer-generated imagery (CGI) models. There is also a dedicated modelling agency for digital models called The Digital.

Amazon, the largest online retailer by revenue, has 45 000 robots at its warehouses to fulfil orders and a fleet of airborne drones into service for fast deliveries. It is not just online that retail is transforming with the 4IR. There is room to implement this kind of technology at brick-and-mortar level. The introduction of robotics has streamlined checkout processes, for instance. In the United Kingdom, you can self-checkout at grocery stores that weigh your goods to prevent theft. Similarly, there are robots akin to sales assistants in stores in the United States they can help you find an item either verbally or through the touch screen. Some robots can perform real-time inventory tracking.

Best Buy, the US-based electronics store, has an automated system much like the claw machine at the arcade that can retrieve products from shelves. There is scope to streamline and automate processes that will prove to be cost-effective for retailers in the long run. Accelerated adoption of technology will be a key strategic move that could lift retailers margins significantly. Retailers can introduce digital technologies and automation into their operations to reduce costs and enhance the customer experience. They can turn e-commerce from a threat to a growth opportunity, a McKinsey and Company report on the future of work in South Africa reads.

One of the grim realities of this era we are moving into is that there will be knock-on employment, particularly of low-skill workers. The caveat is that there will be demand for graduates and employees with higher skills levels, and we need to meet the demand for graduates not to fall into an even deeper unemployment crisis.

From a retail perspective, there is so much to be done that can augment consumers experiences. As industries vie to be a step ahead in the ever-changing context, consumers and business owners have to be open to these experiences and shifts. As physicist William Pollard once said: Without change there is no innovation, creativity, or incentive for improvement. Those who initiate change will have a better opportunity to manage the change that is inevitable.

Professor Tshilidzi Marwala is the vice-chancellor and principal of the University of Johannesburg and deputy chair of the Presidential Commission on the Fourth Industrial Revolution

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Google’s AI Vision May No Longer Include Giant Robots – The Ringer (blog)

(Getty Images/Ringer illustration)

Good news for the deeply paranoid among us: If the apocalypse arrives via giant anthropomorphic robots, they probably wont be bankrolled by Google. On Thursday, Googles parent company, Alphabet, announced that it was selling Boston Dynamics, its premier robotics division, to the Japanese telco giant SoftBank for an undisclosed sum. The deal also includes a smaller robotics company called Schaft.

Boston Dynamics was less a moonshot than a sci-fi horror brought to life. Even before being acquired by Google in 2013, the 25-year-old company had already developed a Beast Warsstyle squadron of robot predators with names like BigDog and WildCat, as well as a humanoid model called Atlas. The machines were often developed for the Pentagon under contracts with agencies such as the Defense Advanced Research Projects Agency. Google and the government both said the robots were being tested for disaster-relief scenarios, but that never stopped the stream of headlines describing them as scary, nightmare-inducing, or evil.

Whether Googles ultimate plans were benign or nefarious, they never properly got off the ground. Both Boston Dynamics and Schaft were part of a months-long spending spree Google bankrolled to appease Andy Rubin, the creator of Android, who was looking to robots as his next frontier for innovation. But Rubin left Google in 2014, creating a leadership vacuum as the company struggled to get its various robotics acquisitions headquartered around the world to work in tandem. Under Rubin, Google reportedly had plans to launch a consumer robotics product by 2020, but that timeline seems in doubt now. (Alphabet still owns several smaller robotics startups that specialize in areas such as industrial manufacturing and film production.)

In the years since the Boston Dynamics acquisition, Google has shown that it doesnt need to build a robot butler (or soldier) to create a future dominated by artificial intelligence. Machine-learning algorithms now guide most of the companys products, whether recommending YouTube videos, identifying objects in users photo libraries, or whisking people around in driverless cars. The company is partnering with appliance manufacturers like General Electric so that people can control their ovens via voice commands to Google Home. And most ambitiously, at this years Google I/O, the company unveiled a suite of new products related to its machine-learning framework, TensorFlow. Developers will soon be able to make use of the same AI engines that power Googles products to improve their own offerings via the companys cloud-computing platform.

In the companys ideal future, every human-machine interaction will be powered by Google, even if a specific app or appliance doesnt have Googles name on it. Terminator-style robots (OK, hopefully Jetsons-style) may one day be part of that vision, but the company can easily build an AI army with the products that fill our homes and garages today.

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For The First Time Ever, A Drug Developed By AI Will Be Tested In Human Trials – Forbes

In a world first, a medicine developed by artificial intelligence may be used to treat patients with obsessive-compulsive disorder. The news is remarkable and hints that in the future, AI may help drug development become faster and more efficiently than ever before.

The first non-man made drug molecule, DSP-1181, has now entered Phase 1 clinical trials, European Pharmaceutical Review reported. The molecule is a long-acting potent serotonin 5-HT1A receptor agonist and was developed using AI that was the product of a partnership between Japans Sumitomo Dainippon Pharma and Exscientia in the UK. The compound was developed in a remarkable time, with AI able to complete in 12 months what typically takes five years.

We are very excited with the results of the joint research that resulted in the development of candidate compounds in a very short time, said Toru Kimura, Senior Executive Officer and Senior Executive Research Director of Sumitomo Dainippon Pharma. We will continue to work hard to make this clinical study success so that it may deliver new benefits to patients as soon as possible.

The drug was created by using algorithms, which AI was able to sift through faster than any human could.

Binary code symbols are seen on a laptop screen in this photo illustration on October 15, 2018 in ... [+] Warsaw, Poland. (Photo by Jaap Arriens/NurPhoto via Getty Images)

"There are billions of decisions needed to find the right molecules and it is a huge decision to precisely engineer a drug," Exscienta chief executive Prof Andrew Hopkins told the BBC. "But the beauty of the algorithm is that they are agnostic, so can be applied to any disease," he added.

This is not the first time that AI has played a hand in medicine, as increasing research shows AIs ability to accurately diagnose disease, sometimes even better than doctors. For example, in the case of breast cancer diagnoses, a publication in the journal Nature showed that a computer model using an algorithm was more successful than radiologists reading mammograms, the BBC reported. The study concluded that the AI was as good as the current system, which uses two doctors to reach a single mammogram, but better than a single doctor. The AI also had fewer false negatives than the doctors, which is important as undiagnosed cancer is serious.

In the case of developing drugs, DSP-1181 may have been the first but scientists are convinced it is far from the last.

"This year was the first to have an AI-designed drug but by the end of the decade all new drugs could potentially be created by AI," said Hopkins, BBC reported.

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For The First Time Ever, A Drug Developed By AI Will Be Tested In Human Trials - Forbes

Deloitte predicts machine intelligence, not mere AI, as a big trend for … – VentureBeat

Conventional wisdom holds that artificial intelligence is the next great horizontal technology that will unleash futurewaves of innovation. Yet AI is not a single type of technology. It takes many forms and encompasses many, many uses. Andto focus on AI is to miss the forest for the trees.

The foresthere ismachine intelligence,or MI,according to Deloittes annual Tech Trends report, which was released today. Business spending on MI is forecast to reach $31.3 billion by 2019, according to IDC.

Deloittesreport,Tech Trends 2017: The Kinetic Enterprise, describesAI as a subset of a larger, more important category of technologies (MI) that also include machine learning, deep learning, cognitive analytics, robotics process automation (RPA), and bots, to name a few. Collectively, these and other tools constitute machine intelligence: algorithmic capabilities that can augment employee performance, automate increasingly complex workloads, and develop cognitive agents that simulate both human thinking and engagement, the report states.

Deloitte cites three factors driving the rise of MI:

Collectively, MI technologies like speech recognition, natural language processing, and machine learning will help businesses automate manytasks traditionally done byhumans, thereby driving greater efficiency and productivity. Large tech companies like Alphabet, Amazon, and Apple are betting on delivering these services to businesses. In turn, venture capital firms have loaded their portfolios with MI-focused startups at the bottom of this food chain (see table below).

Above: Machine intelligences impact: Sample acquisitions and investments, 20142016

Deloittes 2016 Global CIO Survey had asked 1,200 IT executives to name newtechnologies in which they planned to invest significantly overthe next two years: 64percent of those included cognitive technologies, or MI.

The report includes some advice for businesses looking to embrace MI. Amazons Maria Renz, vice president and technical adviser to the CEO, and Toni Reid, director of Amazon Alexa, write: We advise looking at your customer base, listening to them, and understanding their core needs and ways in which you can make their lives easier dont be afraid to invent on the customers behalfcustomers dont always know what to ask for. If you have the right focus on the customer experience, the rest should fall into place.

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Deloitte predicts machine intelligence, not mere AI, as a big trend for ... - VentureBeat