Category Archives: Ai

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Cisco today unveiled updates across its Webex portfolio of communications products, including an integrated asynchronous camera feature, AI-powered sound, video enhancements, and a management service for hybrid in-person and virtual events. The companys upgrades are designed to power events and meetings at scale while maintaining interoperability with Zoom, Microsoft Teams, Google Meet, and other third-party meeting platforms.

Headwinds from the pandemic have driven the value of the global videoconferencing market in 2021 to an estimated$6.03 billion. Sixty percent of respondents to an Owl Labs survey reported participating in video meetings more often in 2020 than in 2019, a bigger rise than other workplace staples like email saw year-over-year. Dovetailing with this, hybrid events are expected to continue to have a presence in work life, with 79% of companies expecting to host events that include a virtual attendance option, according to Martech.

Technology has many powers, and the greatest of all is its ability to connect people and level the playing field for so many across the globe, Cisco security and collaboration executive VP and general manager Jeetu Patel said in a statement. Our new Webex innovations mark a significant step forward in helping our customers unlock the potential of their hybrid workforce enabling them to collaborate in new ways and drive [an] inclusive experience.

Cisco is rolling out AI-powered audio intelligence in Webex, leveraging an AI algorithm to optimize all participants voices during meetings. The setting equalizes voices regardless of how close theyre to their devices, automatically differentiating intended speech from background noise.

Another AI-powered feature, People Focus, will be available in December. It will provide better clarity and optimized visuals of in-room attendees facial gestures and body language, Cisco says. Additional camera-related enhancements coming in early 2022 will further improve the interface in meeting rooms, including showing conference room participants in individual boxes on-screen regardless of which meeting platform they use.

In related news, Webex Assistant, Ciscos virtual meeting tool, now supports French, German, Spanish, and Japanese in addition to English. In August, it gained the ability to translate closed captions from English into more than 100 languages with a paid add-on. And starting this week, developers can work with partners to design custom commands for Webex Assistant running on Ciscos Webex devices such as desktop cameras, headsets, and conference room phones.

Vidcast, an asynchronous communication service, also joins the list of new Webex features. Currently in beta at Vidcast.io ahead of integration with the Webex App in Spring 2022, it provides the ability to record, watch, comment, and react to meeting clips on-demand.

Meanwhile, Webexs new Whiteboarding tool enables users to create, find, edit, and share whiteboards with anybody, not excluding people outside their organization. Users can annotate using any device mobile, tablet, laptop in addition to Webex devices.

Webex also now features Collaboration Insights, offering personalized details like the top ten people a user collaborates with weekly, new colleague titles, and more. Two complementary capabilities Well-being and Cohesion in the previously announced People Insights tab give teams a view into anonymous work time patterns, sentiment ratings, and focus time goals. Exclusively for Webex Suite customers, theres Thrive Reset, a collection of wellness topics, and a gallery where users can upload photos. Its based on research showing that it takes 60 to 90 seconds to course-correct from stress, Cisco says, and designed to help users focus on breathing, reflect on what theyre grateful for, reframe problems, or simply stand up and stretch.

[W]hen we provide insights to an individual, the individual owns the data, not the organization because we dont believe that without your explicit permission, youd want to have your boss see that, Patel told VentureBeat in a previous interview regarding Webexs new monitoring features. Engagement should not be measured based on having a judgment on someone saying, Im judging that you look sad, and therefore Im going to do certain things at that point in time, in my mind, you could cross a boundary where theres more bad that can come out of that than good Theres a fine line between This is super productive and We cant do this because it violates my privacy, or its just outright creepy.'

Following Ciscos acquisitions of Socio Labs and Slido earlier this year, the company unveiled an expanded Webex Events product targeting enterprises hosting hybrid events. Management capabilities spanning badging and printing for ticketing, monetization, and networking are available, and customers can now host events via Webex Webinars with Slidos polling, quizzing, and Q&A technology up to 10,000 attendees (in webinar mode) or 100,000 (in webcast mode) in size.

Today, Cisco also announced its 60-plus new partner integrations to Webex including Smartsheet, Hacker Rank, Thrive Reset, Miro, and Mural.

Against this backdrop, new Webex devices are coming to market among them the Webex Desk Mini. The Webex Desk Mini, which comes in a range of colors, features a 15.6-inch, 1080p interactive display; a 64-degree HD camera; a full-range speaker; and a background noise removal mic array. Meanwhile, the new Webex Board Pro sports dual 4K cameras, directional audio, two active styluses, and a choice of a 55- or 75-inch display.

Webex Desk Mini will be available to order in early 2022 for $1,695. Existing Webex enterprise customers will receive the cloud promo price of $999. The Webex Board Pro will launch in available in November, priced at $11,995 (55 inches) and $19,995 (75 inches).

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Cisco launches new AI-powered and hybrid event features for Webex - VentureBeat

During the past two years, we have seen the world drastically change. COVID-19 forced businesses to shift their operations and transition primarily into digital spaces. Unfortunately, this change has been difficult for many distributors who, until recently, relied heavily on face-to-face interactions and traditional sales tactics.

According to the Distribution Strategy Group 2021 State of Sales in Distribution survey, less than half of businesses surveyed were satisfied with their organizations sales performance. The main complaint was forced changes to sales processes in light of COVID-19. Their sales teams were suddenly expected to perform their jobs digitally, and many of them did not have the preparation or training to do so.

Fortunately, these changes dont have to impact your business negatively. Increasing your use of AI and other digital tools will not only help your team be more productive it will also improve sales, cut costs and help you interact more effectively with your customers.

Finding the right tools in 2021 and beyond is essential for distributors who want to efficiently transition their sales teams into a post-COVID environment.

A recent McKinsey survey of 2,500 B2B companies stressed the need for businesses to shake up their sales models by adopting AI and other technologies into the workplace. Here are three ways distributors can improve operations:

Online algorithms and programs are constantly evolving. Every day consumers see digital ads that are tailored to their tastes and interests. The same applies to buyers in the distribution industry. Customers dont want to waste their time they want to get straight to the point and find a product that can solve the problem they have now.

The McKinsey survey found that 75 percent of top performers are solution sellers, meaning they consult with their clients to find the best product for their needs before making a recommendation. According to the survey, Companies strongly or moderately effective at solution selling are 1.5 times more likely to be outperformers.

AI technology is one of the best resources for distributors wanting their sales reps to improve their consultative strategies. Over time, the AI program will monitor clients and analyze how they interact with your company and competitors. Eventually, it will be able to predict your clients needs, offer accurate cross-selling recommendations and proactively remind your team about upcoming re-orders. Compared to competitors, the McKinsey survey states that Outperformers are 62% more effective in using digital tools.

AI can help your salespeople improve client relationships, make great recommendations and provide timely solutions.

Your clients want to feel like they are important. This sentiment is true for everyone even your least-profitable customers. Unfortunately, sales reps often get so caught up with large clients that they may overlook the needs of mid- and lower-level accounts. Over time, this can lead to long-term retention issues. To avoid losing clients entirely, your team needs to have the right tools at their disposal.

According to the McKinsey survey, top distributors have learned how to use digital tools to keep track of their clients and improve their understanding of each account. These insights include:

Your team can then utilize this data to interact with clients more meaningfully, boosting sales and improving loyalty.

The survey states that 3 out of 4 outperformers apply tables stakes analytics such as sales planning, (vs. half of slow growers). Additionally, 2 out of 3 outperformers apply analytics use cases to be more granular on deal and account level opportunities (vs. half of slow growers).

AI can find all of this information for you so your sales reps can focus on speaking to clients and closing deals, rather than digging through data.

Doing business online is a necessity in 2021. According to the McKinsey survey, 42 percent of outperformers generate more than half their revenue through digital channels. Additionally, 68 percent of outperformers combine traditional and digital channels in the customer journey.

Unfortunately, one of the biggest hurdles distributors face in transitioning to a more digital environment is implementation. Before COVID-19, in-person sales meetings were the norm for most distributors. Distributors were forced to find another way to engage with customers during the pandemic. Now that many teams have taken a hybrid approach to sales, some are struggling to adapt.

A good implementation begins by picking the right tool one that saves sales reps time or helps them make more money from the right company one that is committed to making implementation as smooth as possible. When sales reps see whats in it for them they will be more likely to use the tool.

To ensure this transition is a positive experience for your team, it is necessary to educate team members on the importance of digital tools and provide proper training and support. Start by introducing the tool to the management team. Then slowly roll out the technology to the sales and customer service reps. Identify the feature that will provide the most value to the sales team and introduce that first. Once the team experiences the value of using a feature firsthand, they will be more willing to try others. Incentives for using the tool also help to increase adoption rates.

Additionally, it should be easy for sales reps to get ongoing help from the vendors support staff. Ensure sales reps can access resources, such as written documentation and videos, when they have questions.

AI can drastically boost sales, improve ROI and help sales teams become more effective.

It is important to demonstrate to your sales team that AI can help make their jobs easier while improving sales and commissions. According to an article published by Forbes, By harnessing the power of AI, a sales team can spend less time chasing down the wrong leads, boosting overall productivity and success.

Benj CohenCOVID-19 has forever changed the way distributors operate. Although the changes werent planned, they can contribute to a positive transformation for sales teams. With the right tools, distributors can take advantage of this new digital environment to become more successful than ever.

Benj Cohen founded Proton to help distributors harness cutting-edge artificial intelligence. He learned about distribution firsthand at Benco Dental, a business started by his great grandfather. Hes on a mission to supply distributors with an innovative technology they need to thrive in modern markets. Contact Benj atbenj@proton.aior visitproton.ai.

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How Leveraging AI Can Transform Distribution Sales - Industrial Distribution

Playing Flappy Bird on the Raspberry Pi is a simple feat in itself but this project takes things a step further by using artificial intelligence to play it for you. Created by maker Dmytro Panin, also known as Dr2mod at Reddit, this project acts as a standalone display that continuously plays through the classic game.

Sometimes the best Raspberry Pi projects have simple inspirations. Dr2mod explained the project idea derived from a desire to create something interesting and decorative to look at alongside the window by his desk. The end result was this self-playing Flappy Bird project.

The project relies on an SPI display for video output. It implements an AI algorithm from a paper known as Evolving Neural Networks through Augmenting Topologies, often referred to as NEAT, to navigate the map and guide flappy bird through the course of pipes. This Flappy Bird game is actually a clone designed by Panin just for the NEAT AI to operate.

According to Panin, the AI has a chance of failure. During some test runs, one agent failed to clear a pipe with a score of around 9,000. That said, hes also seen some reach scores as high as 30,000.

Panin was kind enough to share the source code at GitHub for anyone interested in checking out the project in greater detail. If you enjoyed this project, be sure to follow Panin for more cool Raspberry Pi projects.

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Raspberry Pi Plays Flappy Bird With the Power of AI - Tom's Hardware

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Bill Would Block Contractors from Selling Data Harvested with AI Tools to Third Parties - Nextgov

The quest for national AI success has electrified the worldat last count, 44 countries have entered the race by creating their own national AI strategic plan. While the inclusion of countries like China, India, and the U.S. are expected, unexpected countries, including Uganda, Armenia, and Latvia, have also drafted national plans in hopes of realizing the promise. Our earlier posts, entitled How different countries view artificial intelligence and Analyzing artificial intelligence plans in 34 countries detailed how countries are approaching national AI plans, as well as how to interpret those plans. In this piece, we go a step further by examining indicators of future AI needs.

Clearly, having a national AI plan is a necessary but not sufficient condition to achieve the goals of the various AI plans circulating around the world; 44 countries currently have such plans. In previous posts, we noted how AI plans were largely aspirational, and that moving from this aspiration to successful implementation required substantial public-private investments and efforts.

In order to analyze the implementation to-date of countries national AI objectives, we assembled a country-level dataset containing: the number and size of supercomputers in the country as a measure of technological infrastructure, the amount of public and private spending on AI initiatives, the number of AI startups in the country, the number of AI patents and conference papers the countrys scholars produced, and the number of people with STEM backgrounds in the country. Taken together, these elements provide valuable insights as to how far along a country is in implementing its plan.

As analyzing each of the data elements individually presented some data challenges, we conducted a factor analysis to determine if there was a logical grouping of the data elements. Factor analysis reveals the underlying structure of data; that is, the technique mathematically determines how many groups (or factors) of data exist by analyzing which data elements are most closely related to other elements.

Given that our data included five distinct dimensions (i.e., technology infrastructure, AI startups, spending, patents and conference papers, and people), we expected that five factors would emerge, particularly since the dimensions appear to be relatively separate and distinct. But the data showed otherwise. In all, this factor analysis revealed all of the data elements fall under two factorspeople-related and technology-related.

The first factor is the set of AI hiring, STEM graduates, and technology skill penetration data points, which are all associated with the people side of AI. Without qualified people, AI implementations are unlikely to be effective.

The second factor is comprised of all the non-people data elements of AI, which include computing power, AI startups, investment, conference and journal papers, and AI patent submission data points. In looking at these data elements, we realized that all of the data elements in this factor were technology-related, either from a hardware or a thought-leadership standpoint.

Given these findings, we can treat the data as containing two distinct categories: people and technology. Figure 1 shows where a select set of countries sit along these dimensions.

The countries that are in the upper right-hand corner we dub Leaders; they have both the people (factor 1) and the technology (factor 2) to meet their goals. Countries in the lower right quadrant we dub Technically Prepared, and because they are higher on the technology dimensions (factor 2) but lower on the people dimensions (factor 1). Those countries in the upper left quadrant we dub the People Prepared, and largely due to their factors higher on the people dimension (factor 1), but lower on the technology dimension (factor 2). The final quadrantthe lower left quadrantwe dub the Aspirational quadrant since those countries have not yet substantially moved forward in either the people or technology dimension (factor 1 and 2 respectively) in achieving their national AI strategy.

China is unmistakably closer to achieving its national AI strategy goals. It is both a leader in the technical dimension and a leader in the people dimension. Of note is that, while China is strongly positioned in both dimensions, it is not highest in either dimension; the U.S. is higher in the technical dimension, and India, Singapore, and Germany are all higher on the people dimension. Given the population of China and its overall investment in AI-related spending, it is not surprising that China has an early and commanding lead over other countries.

The U.S., while a leader in the technology dimension, particularly in the sub-dimensions of investments and patents, ranks a relatively dismal 15th place after such countries as Russia, Portugal, and Sweden in the people dimension. This is especially clear in the sub-dimension of STEM graduates, where it ranks near the bottom. While the vast U.S. spending advantage has given it an early lead in the technology dimensions, we suspect that the overall lack of STEM-qualified individuals is likely to significantly constrain the U.S. in achieving its strategic goals in the future.

By contrast, India holds a small but measurable lead over other countries in the people dimension, but is noticeably lagging in the technology dimension, particularly in the investment sub-dimension. This is not surprising, as India has long been known for its education prowess but has not invested equally with leaders in the technology dimension.

Our focus on China, the U.S., and India is not to suggest that these are the only countries that can achieve their national AI objectives. Other countries, notably South Korea, Germany, and the United Kingdom are just outside of top positions, and, by virtue of generally being well-balanced between the people and the technology dimensions, have an excellent chance to close the gap

At present, China, the U.S., and India are leading the way in implementing national AI plans. Yet China has already hit on a balanced strategy that has thus far eluded the U.S. and India. This suggests that China needs to merely continue its strategy. However, strategy refinement is necessary for the U.S. and India to keep pace. These leaders are closely followed by South Korea, Germany, and the United Kingdom.

In future posts, we will dive deeper into both the people and technology dimensions, and will dissect specific shortfalls for each country, as well as what can be done to address these shortfalls. Anything short of a substantial national commitment to AI achievement is likely to relegate the country to the status of a second-tier player in the space. If the U.S. wants to dominate this space, it needs to improve the people dimension of technology innovation and make sure it has the STEM graduates required to push its AI innovation to new heights.

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Winners and losers in the fulfilment of national artificial intelligence aspirations - Brookings Institution

Many people today are stressed over what AI means for their future employment, particularly those who have lived in their comfort zone for many years performing the same old tasks at work. For people who can embrace the unpredictable nature of life and thus recognize the continual requirement for flexibility in thought and action, as well as a desire to learn new things (skills, competencies, and knowledge) in new areas, AI at work is something to be excited about. This is particularly true if youre able to think about and analyze whats going on around you since it will help you spot possible opportunities to take advantage of your talents and determine what new skills and knowledge are required.

This demand for flexibility and adaptability is set to increase as workforce automation grows, according to the latest research from Gartner. People need to develop certain skill sets that cant be performed well by AI, at least with current technology.

These skillsets include creativity, social and emotional intelligence, sensing, computational thinking, and deconstruction. These categories are supposed to help build an understanding of the human capabilities that are not as easily replicated by machines (yet).

Even if AI develops these capacities, humans should still be able to distinguish themselves from machines by being able to modify the application of these new talents/knowledge with the necessary interpersonal/soft skills for genuine job success. This brings about the following question: What responsibilities, duties, and activities can be automated to make things go more smoothly?

AI is expected to help humans in the future, not replace them. People will develop their capabilities and talents much more by applying these skills to new technologies and developments. This may lead us into a digital economy where people create things rather than produce them since AI can create multiple models in just minutes. Computational thinking and deconstruction are definitely skills many people dont currently possess because they dont need them to perform their jobs. This can be a problem when AI starts to progress and compete with humans for jobs.

Duties, responsibilities, and activities can be automated, but this depends on what industry you work in. For example, drivers could be replaced by self-driving cars, which are becoming more popular, but it is less likely for AI to replace preschool teachers. That is because teaching requires creativity and social and emotional intelligence skills that machines cant yet perform.

The future of humanity is and will always be human. The question should be, How can AI coexist with a human future? Not, How will people interact with AI in the future? How can we meld the distinct abilities of AI systems and people to discover a human specialty? The answer to these questions is multifaceted:

Humans should embrace computing and AI as part of their future while fighting to keep what makes them unique, such as creativity and social intelligence. Furthermore, humans need to recognize how they can use their unique skills to improve AI systems rather than feeling threatened by them.

AI can be the language that connects people who have difficulties understanding each other to help them collaborate better. The success of people who work with AI depends on how well they understand the working principles of this technology.

Cross-functional alignment and collaboration are essential to growth. Multi-disciplinary knowledge of AI and its impact on society, business strategy, industries, and public policies is essential. In the new age, we have to focus on creativity and be better than machines. We must look into solutions that allow humans and machines to complement each other, maximizing the potential of both.

So far, machines cannot really replicate the imagination. Elevating humans to higher-value tasks benefits organizationsand any individuals career progression and work satisfaction. Machines can liberate human employees to do more meaningful work, which is why research estimates U.S. productivity will rise 40% by 2035.

Understanding the differences between AI and humans will be one of the most crucial skills for the future. For example, well need to identify which parts of an organization should be managed by AI and which should be managed by human beings.

With this in mind, lets look at what machines can currently do better than humans. Right now, machines are superior at ongoing monitoring: They will have a lower error rate than humans at repetitive tasks and more consistent information gathering and analysis. That develops a vast knowledge base that is easily accessible.

That said, humans can develop fruitful partnerships with machines in the following ways:

Creative problem-solving: Humans can use their emotional intelligence to interact with the machine using natural language and have the machine understand them. This would help with problem-solving as the machine will have a deeper understanding of the humans concerns.

Emotional intelligence: As humans embrace computing, they should learn more about AI and how it works with machines more effectively. It is like building a bond between you and your car: If you cannot understand how the car works, it is hard to become friends with it.

Computational thinking: AI can understand human emotions and context because it understands natural language processing and computational capabilities. On the human front, understanding the design principles that underlie computing systems would help people working within these systems understand how they work to build upon them.

The future of jobs depends on how well people understand AI and use their unique skills to improve AI systems rather than being threatened by them. Having clear roadmaps for individuals to learn computing will help humans and AI come closer together.

Mark Minevich is Chief Digital Strategist, International Research Center on AI under auspices of UNESCO, Sr. Advisor, BCG, member of WEF GFC on AI, B20/G20.

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The future of jobs in the era of AI - Fast Company

Artificial intelligence (AI) has taken on many new shapes and use cases as experts learn more about whats possible with big data and smart algorithms.

Todays AI market, then, consists of a mixture of tried-and-true smart technologies with new optimizations and advanced AI that is slowly transforming the way we do work and live daily life.

Read on to learn about some artificial intelligence trends that are making experts most excited for the future of AI:

More on the AI market: Artificial Intelligence Market

With its ability to follow basic tasks and routines based on smart programming and algorithms, artificial intelligence is becoming embedded in the way organizations automate their business processes.

AIOps and MLops are common use cases for AI and automation, but the breadth and depth of what AI can automate in the enterprise is quickly growing.

Bali D.R., SVP at Infosys, a global digital services and consulting firm, believes that AI is moving toward a certain level of hyper-automation, partially in response to the unexpected changes in manual data and procedures caused by the pandemic.

We are in the second inflection point for AI as it graduates from consumer AI, towards enterprise-grade AI, D.R. said. Being exposed to an over-reliance on manual procedures, such as mass rescheduling in the airline industry, unprecedented loan applications in banks, etc., the industries are now turning to hyper-automation that combines robotic process automation with modern machine learning to ensure they can better handle surges in the future.

Although AI automation is still mostly limited to interval and task-oriented automation that requires little imagination or guesswork on the part of the tool, some experts believe we are moving closer to more applications for intelligent automation.

David Tareen, director for artificial intelligence at SAS, a top analytics and AI software company, had this to say about the future of intelligent automation:

Intelligent automation is an area I expect to grow, Tareen said. Just like we automated manufacturing work, we will use AI heavily to automate knowledge work.

The complexity comes in because knowledge work has a high degree of variability. For example, an organization will receive feedback on their products or services in different ways and often in different languages as well. AI will need to ingest, understand, and modify processes in real-time before we can automate knowledge work at large.

AI, automation, and the job market: Artificial Intelligence and Automation

Because of the depth of big data and AIs reliance on it, theres always the possibility that unethical or ill-prepared data will make it into an AI training data set or model.

As more companies recognize the importance of creating AI that conducts its operations in a compliant and ethical manner, a number of AI developers and service providers are starting to offer responsible AI solutions to their customers.

Read Maloney, SVP of marketing at H2O.ai, a top AI and hybrid cloud company, explained what exactly responsible AI is and some of the different initiatives that companies are undertaking to improve their AI ethics.

AI creates incredible new opportunities to improve the lives of people around the world, Maloney said. We take the responsibility to mitigate risks as core to our work, so building fairness, interpretability, security, and privacy into our AI solutions is key.

Maloney said the market is seeing an increased adoption of the core pillars of responsible AI, which he shared with Datamation:

Companies are exploring several ways to make their AI more responsible, and most are starting with cleaning and assessing both data sets and existing AI models.

Brian Gilmore, director of IoT product management at InfluxData, a database solutions company, believes that one of the top options for model and data set management is distributed ledger technology (DLT).

As attention builds around the ethical and cultural impact of AI, some organizations are beginning to invest in ancillary but important technologies that utilize consensus and other trust-ensuring systems as a part of the AI framework, Gilmore said. For example, distributed ledger technology provides a sidecar platform for auditable proof of integrity for models and training data.

The decentralized ownership, distribution of access, and shared accountability of DLT can bring significant transparency to AI development and application across the board. The dilemma is whether for-profit corporations are willing to participate in a community model, trading transparency for consumer trust in something as mission critical as AI.

See more: The Ethics of Artificial Intelligence (AI)

Up to this point, AI has most frequently been used to optimize business processes and automate some home routines for consumers.

However, some experts are beginning to realize the potential that AI-powered models can have for solving global issues.

Read Maloney at H2O.ai has worked with people from a variety of industries to brainstorm how AI can be used for the greater good.

We work with many like-minded customers, partners, and organizations tackling issues from education, conservation, health care, and more, Maloney said. AI for good is fundamental to not only our work, including current work on climate change, wildfires, and hurricane predictions, but we are seeing more and more AI for good work to make the world a better place across the AI industry.

Some of the most exciting applications of altruistic AI are being implemented in early education right now.

For instance, Helen Thomas, CEO ofDMAI, an AI-powered health care and education company, offers an AI-powered product to ensure that preschool-aged children are getting the education they need, despite potential pandemic setbacks:

On top of pre-existing barriers to preschool education, including cost and access, recent research findings suggest children born during the COVID-19 pandemic display lower IQ scores than those born before January 2020, which means toddlers are less prepared for school than ever before.

DMAI DBA Animal Island Learning Adventure (AILA) is changing this with AI. [Our product] harnesses cognitive AI to deliver appropriate lessons in a consistent and repetitious format, supportive of natural learning patterns

Recognizing learning patterns that parents might miss, the AI creates an adaptive learning journey and doesnt allow the child to move forward until theyve mastered the skills and concepts presented. This intentional delivery also increases attention span over time, ensuring children step into the classroom with the social-emotional intelligence to succeed.

More on this topic: How AI is Being Used in Education

Internet of Things (IoT) devices have become incredibly widespread among both enterprise and personal users, but what many tech companies still struggle with is how to gather actionable insights from the constant inflow of data from these devices.

AIoT, or the idea of combining artificial intelligence with IoT products, is one field that is starting to address these pools of unused data, giving AI the power to translate that data quickly and intelligently.

Bill Scudder, SVP and AIoT general manager at AspenTech, an industrial AI solutions company, believes that AIoT is one of the most crucial fields for enabling more intelligent, real-time business decisions.

Forrester has noted that up to 73% of all data collected within the enterprise goes unused, which highlights a critical challenge with IoT, Scudder said. As the volume of connected devices for example, in industrial IoT settings continues to increase, so does the volume of data collected from these devices.

This has resulted in a trend seen across many industries: the need to marry AI and IoT. And heres why: where IoT allows connected devices to create and transmit data from various sources, AI can take that data one step further, translating data into actionable insights to fuel faster, more intelligent business decisions. This is giving way to the rising trend of artificial intelligence of things or AIoT.

Decision intelligence (DI) is one of the newest artificial intelligence concepts that takes many current business optimizations a step farther, by using AI models to analyze wide-ranging sets of commercial data. These analyses are used to predict future outcomes for everything from products to customers to supply chains.

Sorcha Gilroy, data science team lead at Peak, a commercial AI solutions provider, explained that although decision intelligence is a fairly new concept, its already gaining traction with larger enterprises because of its detailed business intelligence (BI) offerings.

Decision intelligence is a new category of software that facilitates the commercial application of artificial intelligence, providing predictive insight and recommended actions to users, Gilroy said. It is outcome focused, meaning a solution must deliver against a business need before it can be classed as DI.

Recognized by Gartner and IDC, it has the potential to be the biggest software category in the world and is already being utilized by businesses across a variety of use cases, from personalizing shopper experiences to streamlining complex supply chains. Brands such as Nike, PepsiCo, and ASOS are known to be using DI already.

Read next: Top Performing Artificial Intelligence Companies

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Artificial Intelligence Trends and Predictions for 2021 | AI Trending Now - Datamation

Architectural Design

The effects of AI on society are not just limited to the workplace. Recently, there has been a lot of talk about how AI will affect our social interactions and how we create and experience social spaces.

AI-driven architecture for social spaces presents us with new opportunities as well as challenges. In his work, Babar Kasam Cazir explores the implications of how artificial intelligence could change socioeconomic dynamics - specifically in event spaces - through its ability to analyze patterns at scale.

Cazir is a prominent Moorish American architect who has spent many years working in and around the entertainment and hospitality industry as the founder of AV hospitality, a casting associate at Sony Pictures, a brand ambassador at Armand de Brignac, and an event organizer.

Cazirs vast experience in design and social events has placed him in a prime position to understand the interactions between race and the design of social spaces and how AI is poised to affect this interaction.

Every facet of our society is becoming increasingly aware of how little things can affect the balance of society with regards to equality and inclusiveness Cazir explains; Artificial Intelligence is playing an increasing role in how architecture works and so it stands to reason that if design and construction has any role to pay in building a more inclusive society, then AI has to be at the forefront of that endeavour as well.

As a Moorish architect in the US, Cazir is clearly well aware of the need for more racially equitable social spaces.

In a recent article in Wired Magazine, titled "The Race to Save AI from Itself," author Steven Levy discusses his latest book on the subject of how artificial intelligence will shape our future and make decisions about who gets access to what. As we see massive companies like Google and Amazon begin to automate decision-making using AI, we realize that Levys prediction is not far off.

The usefulness of social spaces largely depends on the social interaction in these places; weddings, conferences, and parties. Whether the event is for corporate, social, cultural, or entertainment purposes, AI has proven highly beneficial in reducing inequality and making events more beneficial.

Cazir explains that in his experience, AI is gradually increasing the usefulness of social gatherings and conferences by making networking easier. Cazirs point is validated by the increased use of AI in event management to handle things like seating arrangements.

Rather than hope that our events create mutually beneficial connections and business relationships, AI technology is deployed by many event managers to strategically match attendees based on similar interests, experiences, age, or professional goals. Since Networking is one of the main reasons people attend events, AI indeed makes events more useful, as Cazir pointed out.

AIs ability to create personalized experiences for the attendees is key to the way events evolve going forward. Its deep learning capabilities allow event managers to extract deeper attendee insights and data before and during events to aid a more personalized experience.

AIs deep learning capability also has immense potential to increase the inclusiveness of events and social gatherings. As increased calls emerge for AI professionals to eliminate the biases often found in AI, it becomes necessary that with respect to event management apps and programs, AI designers should train AI to focus more on vital data like professional interests and achievements, aspirations, and experience rather than race or sex. The approach towards training AI for these purposes should be intentional in trying to create a balanced representation.

Recently, Twitter exploded with the #emmysowhite hashtag that drew in celebrities like 50 cent after no actors of color won a single Emmy in the lead role and supporting categories, though over 40 were nominated. While there are arguments on both sides of the debate, AI could become a valuable tool for architecting more inclusive events and awards in the future.

People naturally gravitate towards social spaces that represent their core demographic, which is what AI should focus on doing. Perhaps an Inclusiveness quotient may not be too far left field for consideration amongst event planners and managers if we are to host more structurally balanced events.

A social space is a physical or virtual space such as a social center, online social media, or other gathering places where people gather and interact. Our communities most common social spaces are town squares, parks, and other public places like pubs or shopping malls.

Social spaces help regulate the general mood of a community and can help foster more inclusiveness in communities. Designers and architects, therefore, play an essential role in envisioning and realizing a more equitable future.

When you consider how some architectural constructions like the Carbini Green and Robert Taylor Homes in Chicago worked to displace minorities and destroy communities, it becomes apparent how essential this duty is. History is replete with inconsiderate constructions that have increased racial and economic divides.

Design should be a collaborative effort between the architects, policy makers and an inclusive delegation of end-users. Cazir explains. Irrespective of whether the project is a public or private project, a concerted approach will help the architects to detect areas that may affect existing interests or foster more inequality. Design always does one of two things, it either empowers experience for all or limits it for some.

Babar Kasam Cazir

In recent times, we have seen certain sections of society take up arms against certain historical statues or protest the naming of certain public places. People in the LGBTQIA+ community have also made a case for the configuration of bathrooms. In like manner, things are simple as the location and size of a building or construction could be critical considerations that come to the fore when the end-users are involved in the design process.

In an article on Archinect, writer Hannah Wood suggests that AI can play an integral role in this process; Through the use of additional hardware, AI and its augmented reality capabilities can capture and enhance real-world experience. It can enable people to engage with a design prior to construction.

Several top architects and designers, including Cazir, also hold Hannahs view that this process can help select the most appealing proposal based on the end-users experiences with the AI simulation of the construction.

For example, AR could allow the client and a select section of locals to move through and sense different design proposals prior to construction. The Lights and Sounds of a building can be simulated, and the feedback could help reorder the emphasis the architects give to specific elements of their design.

With AIs data analysis proficiency, it could be utilized to identify the need for a project or recommend it. For instance, we could set up AI programs that recommend a new school be built in a neighborhood as the number of children crosses a pre-set threshold.

This program could also help recommend building new parks or social spaces in specific neighborhoods. An AI program of this sort will benefit both governments and individual investors as it could help predict both need and potential profitability.

In Cazirs words, The fight against inequality is one that must seep down to every strata of society. It is an endeavor that should be intentionally conscripted into the progress plan of every profession. Equality and inclusion cannot occur by accident, but by a combination of many small things happening differently at the same time.

Social spaces are one of the few places where society meets without hindrances. The architects and designers of the 21st century have to use every available tool, including AI, to ensure that such interactions occur in an environment devoid of intrinsic or extrinsic bias or discomfort for any group.

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AI, Race, And Architecting More Inclusive Social Spaces - Forbes

Humans find AI to be a frustrating teammate when playing a cooperative game together, posing challenges for teaming intelligence, study shows.

When it comes to games such as chess or Go, artificial intelligence (AI) programs have far surpassed the best players in the world. These superhuman AIs are unmatched competitors, but perhaps harder than competing against humans is collaborating with them. Can the same technology get along with people?

In a new study, MIT Lincoln Laboratory researchers sought to find out how well humans could play the cooperative card game Hanabi with an advanced AI model trained to excel at playing with teammates it has never met before. In single-blind experiments, participants played two series of the game: one with the AI agent as their teammate, and the other with a rule-based agent, a bot manually programmed to play in a predefined way.

The results surprised the researchers. Not only were the scores no better with the AI teammate than with the rule-based agent, but humans consistently hated playing with their AI teammate. They found it to be unpredictable, unreliable, and untrustworthy, and felt negatively even when the team scored well. A paper detailing this study has been accepted to the 2021 Conference on Neural Information Processing Systems (NeurIPS).

When playing the cooperative card game Hanabi, humans felt frustrated and confused by the moves of their AI teammate. Credit: Bryan Mastergeorge

It really highlights the nuanced distinction between creating AI that performs objectively well and creating AI that is subjectively trusted or preferred, says Ross Allen, co-author of the paper and a researcher in the Artificial Intelligence Technology Group. It may seem those things are so close that theres not really daylight between them, but this study showed that those are actually two separate problems. We need to work on disentangling those.

Humans hating their AI teammates could be of concern for researchers designing this technology to one day work with humans on real challenges like defending from missiles or performing complex surgery. This dynamic, called teaming intelligence, is a next frontier in AI research, and it uses a particular kind of AI called reinforcement learning.

A reinforcement learning AI is not told which actions to take, but instead discovers which actions yield the most numerical reward by trying out scenarios again and again. It is this technology that has yielded the superhuman chess and Go players. Unlike rule-based algorithms, these AI arent programmed to follow if/then statements, because the possible outcomes of the human tasks theyre slated to tackle, like driving a car, are far too many to code.

Reinforcement learning is a much more general-purpose way of developing AI. If you can train it to learn how to play the game of chess, that agent wont necessarily go drive a car. But you can use the same algorithms to train a different agent to drive a car, given the right data Allen says. The skys the limit in what it could, in theory, do.

Today, researchers are using Hanabi to test the performance of reinforcement learning models developed for collaboration, in much the same way that chess has served as a benchmark for testing competitive AI for decades.

The game of Hanabi is akin to a multiplayer form of Solitaire. Players work together to stack cards of the same suit in order. However, players may not view their own cards, only the cards that their teammates hold. Each player is strictly limited in what they can communicate to their teammates to get them to pick the best card from their own hand to stack next.

The Lincoln Laboratory researchers did not develop either the AI or rule-based agents used in this experiment. Both agents represent the best in their fields for Hanabi performance. In fact, when the AI model was previously paired with an AI teammate it had never played with before, the team achieved the highest-ever score for Hanabi play between two unknown AI agents.

That was an important result, Allen says. We thought, if these AI that have never met before can come together and play really well, then we should be able to bring humans that also know how to play very well together with the AI, and theyll also do very well. Thats why we thought the AI team would objectively play better, and also why we thought that humans would prefer it, because generally well like something better if we do well.

Neither of those expectations came true. Objectively, there was no statistical difference in the scores between the AI and the rule-based agent. Subjectively, all 29 participants reported in surveys a clear preference toward the rule-based teammate. The participants were not informed which agent they were playing with for which games.

One participant said that they were so stressed out at the bad play from the AI agent that they actually got a headache, says Jaime Pena, a researcher in the AI Technology and Systems Group and an author on the paper. Another said that they thought the rule-based agent was dumb but workable, whereas the AI agent showed that it understood the rules, but that its moves were not cohesive with what a team looks like. To them, it was giving bad hints, making bad plays.

This perception of AI making bad plays links to surprising behavior researchers have observed previously in reinforcement learning work. For example, in 2016, when DeepMinds AlphaGo first defeated one of the worlds best Go players, one of the most widely praised moves made by AlphaGo was move 37 in game 2, a move so unusual that human commentators thought it was a mistake. Later analysis revealed that the move was actually extremely well-calculated, and was described as genius.

Such moves might be praised when an AI opponent performs them, but theyre less likely to be celebrated in a team setting. The Lincoln Laboratory researchers found that strange or seemingly illogical moves were the worst offenders in breaking humans trust in their AI teammate in these closely coupled teams. Such moves not only diminished players perception of how well they and their AI teammate worked together, but also how much they wanted to work with the AI at all, especially when any potential payoff wasnt immediately obvious.

There was a lot of commentary about giving up, comments like I hate working with this thing,' adds Hosea Siu, also an author of the paper and a researcher in the Control and Autonomous Systems Engineering Group.

Participants who rated themselves as Hanabi experts, which the majority of players in this study did, more often gave up on the AI player. Siu finds this concerning for AI developers, because key users of this technology will likely be domain experts.

Lets say you train up a super-smart AI guidance assistant for a missile defense scenario. You arent handing it off to a trainee; youre handing it off to your experts on your ships who have been doing this for 25 years. So, if there is a strong expert bias against it in gaming scenarios, its likely going to show up in real-world ops, he adds.

The researchers note that the AI used in this study wasnt developed for human preference. But, thats part of the problem not many are. Like most collaborative AI models, this model was designed to score as high as possible, and its success has been benchmarked by its objective performance.

If researchers dont focus on the question of subjective human preference, then we wont create AI that humans actually want to use, Allen says. Its easier to work on AI that improves a very clean number. Its much harder to work on AI that works in this mushier world of human preferences.

Solving this harder problem is the goal of the MeRLin (Mission-Ready Reinforcement Learning) project, which this experiment was funded under in Lincoln Laboratorys Technology Office, in collaboration with the U.S. Air Force Artificial Intelligence Accelerator and the MIT Department of Electrical Engineering and Computer Science. The project is studying what has prevented collaborative AI technology from leaping out of the game space and into messier reality.

The researchers think that the ability for the AI to explain its actions will engender trust. This will be the focus of their work for the next year.

You can imagine we rerun the experiment, but after the fact and this is much easier said than done the human could ask, Why did you do that move, I didnt understand it? If the AI could provide some insight into what they thought was going to happen based on their actions, then our hypothesis is that humans would say, Oh, weird way of thinking about it, but I get it now, and theyd trust it. Our results would totally change, even though we didnt change the underlying decision-making of the AI, Allen says.

Like a huddle after a game, this kind of exchange is often what helps humans build camaraderie and cooperation as a team.

Maybe its also a staffing bias. Most AI teams dont have people who want to work on these squishy humans and their soft problems, Siu adds, laughing. Its people who want to do math and optimization. And thats the basis, but thats not enough.

Mastering a game such as Hanabi between AI and humans could open up a universe of possibilities for teaming intelligence in the future. But until researchers can close the gap between how well an AI performs and how much a human likes it, the technology may well remain at machine versus human.

Reference: Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi by Ho Chit Siu, Jaime D. Pena, Kimberlee C. Chang, Edenna Chen, Yutai Zhou, Victor J. Lopez, Kyle Palko and Ross E. Allen, Accepted, 2021 Conference on Neural Information Processing Systems (NeurIPS).arXiv:2107.07630

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Artificial Intelligence Is Smart, but It Doesnt Play Well With Others - SciTechDaily

[co-authors: Marek Oleksyn, Sotysiski Kawecki & Szlzak]

AI - a rushing snowball

Human intelligence has developed over thousands of years. Meanwhile, AI, including deep learning algorithms, is the result of just a few decades of work and development. There is no doubt that this snowball cannot be stopped. Artificial intelligence already has a huge impact on key areas of the economy. It also raises a number of critical legal issues, including those relating to innovation and creativity, faced by states, regulators and competent authorities across the globe. One of the most current legal challenges in this field is the admissibility and conditions for patenting inventions made by or with the use of artificial intelligence algorithms.

What exactly is AI?

To better understand this issue, it is worth bearing in mind that in the legal, technological and economic space there are different definitions and ways of understanding the concept of AI. A fresh approach to defining AI (or - rather - AI system) was presented this year by the European Commission in its proposal for the Artificial Intelligence Act (Regulation) of April 21, 2021. According to this document an AI system means software that is developed with one or more of the techniques and approaches and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with. Under the proposed law such techniques and approaches are to include not only machine learning but also logic- and knowledge-based and statistical approaches as well as Bayesian estimation and search and optimization methods. However, the proposed EU Artificial Intelligence Act deals with other issues - and the unified definition of AI system proposed in it may also have an impact on the assessment of who is the actual author (or co-author) of the invention being filed for patenting.

DABUS case before courts and patent authorities

The issue of the admissibility of patenting inventions made by AI has already received some case law in Europe. The trailblazer in this respect is the case of inventions made by an AI machine called DABUS - created and owned by Dr. Stephen Thaler. He filed patent applications, among others, with the European Patent Office and the UK Intellectual Property Office. The applicant explained in his patent applications that the inventor was an AI machine (DABUS) and that he had acquired the right to patents by ownership of this machine. This approach was not accepted by the patent authorities which explained, among others, that DABUS being a machine cannot be regarded as an inventor. The patent applications were refused. Dr. Thaler appealed against the decisions issued by the UK IPO and the EPO. The England and Wales High Court of Justice (in September 2020) and the Court of Appeal (in September 2021) dismissed Dr. Thalers appeal and denied grant of the patent. Both judgments present many interesting considerations regarding the conditions for granting a patent as well as an entity authorized to file patent application. Although they relate to the UK patent regulations, many of the comments presented seem to be of a more universal nature.

Referral to the UK Supreme Court is still possible here. In turn, the oral hearing before the EPO Board of Appeal is scheduled for December 2021.

A litmus test for AI-made inventions?

The DABUS case in Europe certainly has not had its final touch yet and the world of patent practitioners will follow its further development. The matter appears to be a litmus test for similar cases in the future and may significantly affect the scope and timing of changes to the relevant statutory rules across Europe of filing applications for inventions created by AI.

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AI A Rushing Snowball: Can AI-made Inventions Be Patented? - JD Supra