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To say 2020 has been a year of change is a monumental understatement. Everything from the way we socialize, to how we receive health care, to how we work, has changed massively. The hosts of The Suite Spot Carlos Vargas,Howard HoltonandPaul Lewis spoke with Michael Davidson, Senior Data Scientist, Microsoft about some of the changes that businesses had no choice but to make.

Davidson feels that these challenges provided learning opportunities for businesses. He presented companies shifting to remote work as an example, The hardest gap to overcome is the one you mentioned and thats accepting that its even possible. And once youve done it a few times, it doesnt become rote. I mean every project has its own challenges, but just the belief that you can do it, I think is probably the biggest hurdle for most leadership teams to overcome, Davidson said.

He feels that once companies have seen the positive outcomes of change, it becomes easier to enact more change. Especially when you have to react to some sort of catastrophic change in the operating environment. Davidson suspects that a lot of the workplace changes weve seen may remain, even once social distancing guidelines are a thing of the past. As much Covid is obviously a tragedy of proportions we havent seen, but at the same time like anything in life, hopefully we can learn from it, Davidson noted. He finds it hard to believe that workers will be eager to resume long, inconvenient, and expensive commutes, when working remotely has suited them just fine especially in the tech industry. Its nonsensical, right? Were showing most, many of these jobs can be done at home. And for those that cant Fair enough, but certainly in most of our lines of work, its probably 95% of what it was, Davidson stated.

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How the Role of Artificial Intelligence is Changing During the Pandemic - MarketScale

Machine learning is a fast-growing and successful branch of artificial intelligence. In essence, machine learning is the process of allowing a computer system to teach itself how to perform complex tasks by analyzing large sets of data, rather than being explicitly programmed with a particular algorithm or solution.

In this way, machine learning enables a computer to learn how to perform a task on its own and to continue to optimize its approach over time, without direct human input.

In other words, it's the computer that is creating the algorithm, not the programmers, and often these algorithms are sufficiently complicated that programmers can't explain how the computer is solving the problem. Humans can't trace the computer's logic from beginning to end; they can only determine if it's finding the right solution to the assigned problem, which is output as a "prediction."

There are several different approaches to training expert systems that rely on machine learning, specifically "deep" learning that functions through the processing of computational nodes. Here are the most common forms:

Supervised learning is a model in which computers are given data that has already been structured by humans. For example, computers can learn from databases and spreadsheets in which the data has already been organized, such as financial data or geographic observations recorded by satellites.

Unsupervised learning uses databases that are mostly or entirely unstructured. This is common in situations where the data is collected in a way that humans can't easily organize or structure it. A common example of unstructured learning is spam detection, in which a computer is given access to enormous quantities of emails and it learns on its own to distinguish between wanted and unwanted mail.

Reinforcement learning is when humans monitor the output of the computer system and help guide it toward the optimal solution through trial and error. One way to visualize reinforcement learning is to view the algorithm as being "rewarded" for achieving the best outcome, which helps it determine how to interpret its data more accurately.

The field of machine learning is very active right now, with many common applications in business, academia, and industry. Here are a few representative examples:

Recommendation engines use machine learning to learn from previous choices people have made. For example, machine learning is commonly used in software like video streaming services to suggest movies or TV shows that users might want to watch based on previous viewing choices, as well as "you might also like" recommendations on retail sites.

Banks and insurance companies rely on machine learning to detect and prevent fraud through subtle signals of strange behavior and unexpected transactions. Traditional methods for flagging suspicious activity are usually very rigid and rules-based, which can miss new and unexpected patterns, while also overwhelming investigators with false positives. Machine learning algorithms can be trained with real-world fraud data, allowing the system to classify suspicious fraud cases far more accurately.

Inventory optimization a part of the retail workflow is increasingly performed by systems trained with machine learning. Machine learning systems can analyze vast quantities of sales and inventory data to find patterns that elude human inventory planners. These computer systems can make more accurate probability forecasting for customer demand.

Machine automation increasingly relies on machine learning. For example, self-driving car technology is deeply indebted to machine learning algorithms for the ability to detect objects on the road, classify those objects, and make accurate predictions about their potential movement and behavior.

Continued here:
What is machine learning? Here's what you need to know - Business Insider - Business Insider

WASHINGTON Congress signaled its confidence in the Pentagons young artificial intelligence office through a series of measures to increases its standing in the agency, including giving its director acquisition authority.

The annual defense policy bill, called the fiscal 2021 National Defense Authorization Act, would alter the reporting structure of the Joint Artificial Intelligence Center, raising the office to report directly to the deputy secretary of defense, instead of the departments chief information officer. The bill, which still needs President Donald Trumps approval, establishes a board of advisers to give the center strategic advice and technical expertise on AI matters.

The measures to bolster the importance of the Joint Artificial Intelligence Center come as the organization pivots from focusing on artificial intelligence projects to identifying and solving problems within the services using AI. The JAIC was established in 2018 to increase the adoption of AI across the Pentagon.

Until now, the office hasnt had acquisition authority. The NDAA would authorize a maximum of $75 million for the JAIC director for the development, acquisition and sustainment of artificial intelligence technologies, services and capabilities through fiscal year 2025. This year the JAIC has repeatedly mentioned the challenges that the current acquisition process causes. Its contract awards usually relied on the General Services Administration or Defense Innovation Unit, an entity also meant to speed up the acquisition process.

More autonomy over acquisitions could streamline the process to get the services AI technology faster, said Lindsey Sheppard, a fellow at the Center for Strategic and International Studies.

Finding available contracting vehicles can be a big challenge for technology development efforts, she said in an email. You may have identified a mission need and have a great solution, but no available contract vehicle. And it can take years to get something in place. This NDAA would clear that roadblock by giving the JAIC its own acquisition authorities to get technology in the door.

Air Force Lt. Gen. Jack Shanahan, former director of the JAIC until he retired in June, called for the authority back in May. At the time, he said the lack of acquisition authorities was slowing the agency down when it needed to go faster.

Martijn Rasser, a senior fellow at the Center for a New American Security, told C4ISRNET that the new authority would help bring nontraditional contractors into the fold.

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This will enable JAIC to speed up the process and gives them the opportunity to level the playing field for small and nontraditional tech companies, which is key to ensuring DoD has access to the broadest array of AI solutions as possible, Rasser said.

Elevating the JAIC to a direct report of the deputy secretary is an important step in recognizing the offices importance, experts said, especially as tech priorities could change under a new presidential administration.

The JAIC reporting directly to the deputy secretary of defense says that regardless of how that shuffling comes out in the next few months, AI will still be a significant priority and its place on the org chart reflects that, Sheppard said.

The NDAA also would direct the defense secretary to establish a board of advisers for the JAIC on technical issues, ethical challenges and workforce issues related to AI use. The board, appointed by the secretary and made up of industry and academic experts, also would guide long-term AI studies and strategies. It would meet at least once a quarter and submit a report annually summarizing its work.

This has been an important year for the JAIC as it started its first warfighting initiative and pivoted to play a role in the DoDs COVID-19 response. The center also had a change in leadership this year after Marine Corps Lt. Gen. Michael Groen took over from Shanahan.

Meanwhile, the JAIC also rolled out JAIC 2.0, a realignment of its AI programs, called national mission initiatives, to better match warfighting needs and identify challenges in the services where AI can help.

What we want to do is seek out problems, Groen said last month. If in JAIC 1.0, we built technologies and then tried to find a market for them, [then] in JAIC 2.0, were going to be problem-pull. Were going to build the relationships across the department to help us understand where the most compelling problems are so then we can pull our technology development and enablement in that direction.

Read more here:
Congress wants to boost the prominence of Pentagon's AI center - C4ISRNet

The inventions in Artificial Intelligence are thriving the pace of invention despite the existing pandemic.

The year 2020 has surprised humans in many ways. From encountering a pandemic, addressing a global recession, and witnessing the global geopolitical changes, humanity is standing in ambiguous times. However, not everything is uncertain. Throughout the year, emerging technologies such as artificial intelligence, robotics, Internet of Things, and augmented/virtual reality, amongst others have spearheaded innovation with a promising future. These technologies have validated that despite the crisis, technology will transform the world.

Henceforth, Analytics Insight brings you the major inventions of 2020 that has shaped the world.

BrainBox AIis the most revolutionary technology gifted to humanity. It utilizes self-adapting artificial intelligence technology which proactively optimizes the energy consumption of buildings, which are the largest climate change contributors. Its AI engine supports a self-operating building that requires no human intervention. It optimizes the control of heating, ventilation and air-conditioning, with the use of deep learning, cloud-based computing and automation to create maximum impact on energy consumption. By using BrainBox AI, commercial buildings can reduce the total energy costs by 25%, and improves occupant comfort by 60%.

Additionally, keeping in mind about the current crisis, the AI is designed in a manner, where it reduces the carbon footprint by 20-40%.

There are some diseases which have been detrimental to human health, even before coronavirus surfaced the earth. Cancer is listed amongst the most-deadliest disease, impacting human health. So far there have been no specific techniques that can detect cancer in its early stage. But researchers have made an AI system through which the patients can track the symptoms. TrailJectory uses artificial intelligence to empower patients to own their cancer journey and analyze its global patient communitys accumulated data so that cancer patients take informed decisions. It analyzes all relevant treatment options and instantly presents only what is a relevant treatment plan concerning the patients condition.

Children have witnessed countless incidents of social chaos and apathy this year. The changing societal diaspora, increase in the incidents of hatemongering and racism poses a threat to the childrens learning of compassion and empathy. However, robotic advancements have led to the innovation of a tool that helps the children being kind and compassionate.Emboddied Moxieis a robot that allows the children to be more empathic, kind and develop advanced human skills. Moxie can perceive, process, and respond to natural conversation, eye-contact, facial expressions and other behaviour to create a unique and personalized learning experience for the child. It helps to build verbal confidence amongst children and helps in picking the social-emotional concepts, the theory of mind and comprehension with the help of NLP. Tailor-made for the overall development of children, this nascent robotic tool helps in improving the physical health of the children with activities like breathing, and guided meditation to help develop emotion and self-regulation skills.

The uncertain times inflict melancholic feelings and mental distress, which is detrimental for the overall development of an individual. Especially, if this distress seeps through personal relationships it causes chaos. To eliminate the incidents of disruptive relationship,LOVOTis designed to bring emotional peace at home. This emotional robotic model is a model that it senses the mood of an individual and reacts accordingly for elevating the mood. This is a perfect addition for having a peaceful, lovable and homey environment. The technology uses more than 50 sensors to create a behaviour which is very much like a human being.

The importance of getting reliable data is felt in every business across every sector during COVID-19. Owing to the remote working, it has become impossible for organizations to rely on the traditional methods of data mining. Thats whereApache Hadoopcomes to the scene. Commonly termed as Hadoop Ecosystem, Apache Hadoop crunches the data in a manner that allows distributed processing of large data sets across clusters of computers using simple programming models. Designed to scale up from single servers to thousands of machines, it offers local computation and storage. It is designed to detect and handle failures at the application layer, for delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Widely used by the big techs such as Google and Amazon, the NoSQL database is a mechanism through which a variety of models can access and manage the data. This database is designed specifically for managing and monitoring large data volume, low latency, and flexible data models. It is designed for addressing the limitations in data consistency. It is used in applications such as mobile, web, and gaming that require flexible, scalable, high-performance, and highly functional databases to provide great user experiences.

As the name suggests HTC Vive Pro Eye is a virtual reality component tailored for precision eye-tracking, giving users a premium immersive experience. Like any sci-fi movie, this VR component allows eye movements and blinks in virtual avatars. It shows expressive, non-verbal interactions in conferences, chat groups and remote collaboration, thus bringing customized experiences for the user.

Developed by the beauty brand,LOreal Persois a virtual reality tool that provides personalised beauty care for the user. The LOreal Perso app analyses the overall skin condition which gets alluded by human vision. This includes deep wrinkles, fine lines, the appearance of dark circles and pore visibility. It also assesses the factors such as geographic location, climate situation of the user, and environmental conditions which are impacting the human skin. The app also presents the assortment of LOreal products improving self-care.

In the year 2020, most meetings and conferences have shifted to online platforms. But network connectivity is the major limitation with remote working. Henceforth,Meeting Owlis an Internet of Things integrated robotic owl, custom made for working together from different locations. The Meeting Owl is the best tool for remote working set-up. Consisting of a mic, camera and speaker in only one robotic device, the user just needs to connect it with the laptop for a better experience.

The year 2020 has been a year where everyone got an opportunity to stay with their family, and to spend quality time. To enhance this experience, Kuri Mobile Robots is a home robot integrated with IoT sensors that interact with the user and the family members, and capture moments of every day with sensor-enabled cameras.

Originally posted here:
Top 10 Artificial Intelligence Inventions in 2020 - Analytics Insight