AI News, Streamlining Government Operations with the Power of AI
CHIPS Articles: Artificial Intelligence Helps NIST, FCC Facilitate Spectrum Sharing for US Navy
In a new paper, NIST researchers demonstrate that deep learning algorithms—a form of artificial intelligence—are significantly better than a commonly used, less sophisticated method for detecting when offshore radars are operating.
Broadband providers, such as AT&T, Google, Nokia, Qualcomm, Sony and Verizon, have been eager to access this band (between 3550 and 3700 MHz) because it will expand product markets and give end users better coverage and higher data rate speeds in a variety of environments where service is traditionally weak.
“However,” said Michael Souryal, lead for the NIST spectrum sharing support project, “these energy detectors are not discriminating enough to consistently get it right, sometimes confusing other RF signals as radar or missing the radar signatures altogether.” Souryal and his colleagues considered artificial intelligence for a potential solution.
Eight deep learning algorithms—software systems that learn from pre-existing data—were trained to recognize offshore radar signals from a collection of nearly 15,000 60-second-long spectrograms (visual representations of the radar signals over time).
After training, the deep learning algorithms were matched against energy detectors to see which performed best at identifying and classifying a set of spectrograms different from the ones used to educate the AI detectors, according to the release.
Applications of artificial intelligence
Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society.
More specifically, it is Weak AI, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading, robot control, and remote sensing.
Crop and soil monitoring uses new algorithms and data collected on the field to manage and track the health of crops making it easier and more sustainable for the farmers.
The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.
The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a human being to perform.
Haitham Baomar and Peter Bentley are leading a team from the University College of London to develop an artificial intelligence based Intelligent Autopilot System (IAS) designed to teach an autopilot system to behave like a highly experienced pilot who is faced with an emergency situation such as severe weather, turbulence, or system failure.
Educating the autopilot relies on the concept of supervised machine learning “which treats the young autopilot as a human apprentice going to a flying school”.
The Intelligent Autopilot System combines the principles of Apprenticeship Learning and Behavioural Cloning whereby the autopilot observes the low-level actions required to maneuver the airplane and high-level strategy used to apply those actions.
When students sit at their desk, their devices will be able to create lessons, problems, and games to tailor to the specific student’s needs, particularly where a student may be struggling, and give immediate feedback.
As far as the future of AI in education, there are many new possibilities due to what has been coined by The New York Times as “The Great AI Awakening.” One of these possibilities mentioned by Forbes included the providing of adaptive learning programs, which assess and react to a student’s emotions and learning preferences.
As a whole AI has the power to influence education by taking district, state, national, and global data into consideration as it seeks to better individualize learning for all.
An example of a revenge effect is that the extended use of technology may hinder students’ ability to focus and stay on task instead of helping them learn and grow.
Also, the need for AI technologies to work simultaneously may lead to system failures which could ruin an entire school day if we are relying on AI assistants to create lessons for students every day.
It is inevitable that AI technologies will be taking over the classroom in the years to come, thus it is essential that the kinks of these new innovations are worked out before teachers decide whether or not to implement them into their daily schedules.
Algorithmic trading involves the use of complex AI systems to make trading decisions at speeds several orders of magnitudes greater than any human is capable of, often making millions of trades in a day without any human intervention.
Automated trading systems are typically used by large institutional investors, but recent years have also seen an influx of smaller, proprietary firms trading with their own AI systems.
Its wide range of functionalities includes the use of natural language processing to read text such as news, broker reports, and social media feeds.
For example, Digit is an app powered by artificial intelligence that automatically helps consumers optimize their spending and savings based on their own personal habits and goals.
The app can analyze factors such as monthly income, current balance, and spending habits, then make its own decisions and transfer money to the savings account.
Wallet.AI, an upcoming startup in San Francisco, builds agents that analyze data that a consumer would leave behind, from Smartphone check-ins to tweets, to inform the consumer about their spending behavior.
This class of financial advisers work based on algorithms built to automatically develop a financial portfolio according to the investment goals and risk tolerance of the clients.
An online lender, Upstart, analyze vast amounts of consumer data and utilizes machine learning algorithms to develop credit risk models that predict a consumer’s likelihood of default.
This platform utilizes machine learning to analyze tens of thousands traditional and nontraditional variables (from purchase transactions to how a customer fills out a form) used in the credit industry to score borrowers.
“The major junctions of the system were to monitor premiums in the market, determine the optimum investment strategy, execute transactions when appropriate and modify the knowledge base through a learning mechanism.”
It was able to review over 200,000 transactions per week and over two years it helped identify 400 potential cases of money laundering which would have been equal to $1 billion.
Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading.
In the automotive industry, a sector with particularly high degree of automation, Japan had the highest density of industrial robots in the world: 1,414 per 10,000 employees.
There are three ways AI is being used by human resources and recruiting professionals: to screen resumes and rank candidates according to their level of qualification, to predict candidate success in given roles through job matching platforms, and now rolling out recruiting chat bots that can automate repetitive communication tasks.
TextRecruit, a Bay Area startup, released Ari (automated recruiting interface.) Ari is a recruiting chatbot that is designed to hold two-way text message conversations with candidates.
Ari automates posting jobs, advertising openings, screening candidates, scheduling interviews, and nurturing candidate relationships with updates as they progress along the hiring funnel.
AI-powered engine streamlines the complexity of job hunting by operating information on job skills, salaries, and user tendencies, matching people to the most relevant positions.
Machine intelligence calculates what wages would be appropriate for a particular job, pulls and highlights resume information for recruiters using natural language processing, which extracts relevant words and phrases from text using specialized software.
Typical use case scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for recognizing relevant scenes, objects or faces.
The motivation for using AI-based media analysis can be — among other things — the facilitation of media search, the creation of a set of descriptive keywords for a media item, media content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for the placement of relevant advertisements.
Another artificial intelligence musical composition project, The Watson Beat, written by IBM Research, doesn't need a huge database of music like the Google Magenta and Flow Machines projects, since it uses Reinforcement Learning and Deep Belief Networks to compose music on a simple seed input melody and a select style.
The company Narrative Science makes computer-generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game in English.
Yseop is able to write financial reports, executive summaries, personalized sales or marketing documents and more at a speed of thousands of pages per second and in multiple languages including English, Spanish, French &
Boomtrain’s is another example of AI that is designed to learn how to best engage each individual reader with the exact articles — sent through the right channel at the right time — that will be most relevant to the reader.
The program would start with a set of characters who wanted to achieve certain goals, with the story as a narration of the characters’ attempts at executing plans to satisfy these goals.
Their particular implementation was able faithfully reproduced text variety and complexity of a number of stories, such as red riding hood, with human-like adroitness.
Power electronics converters are an enabling technology for renewable energy, energy storage, electric vehicles and high-voltage direct current transmission systems within the electrical grid.
These converters are prone to failures and such failures can cause downtimes that may require costly maintenance or even have catastrophic consequences in mission critical applications.
Researchers are using AI to do the automated design process for reliable power electronics converters, by calculating exact design parameters that ensure desired lifetime of the converter under specified mission profile.
This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and the first widely released robot, Furby.
The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives..
Project: Visegrad Intelligence – the state of AI in V4
The project conducted by the Institute of Politics and Society together with partners and supported by the aims to map and review existing AI policies, R&D centres and strategies in V4.
It will thus help better coordinate resources and policies, avoid duplicities and formulate joint statements on regional (EU) and global level (UN, OECD) Together we stand, divided we fall Visegrad
Yet, when acting on their own, they can neither implement globally relevant commercial projects, nor can they properly prepare for the political and economical impact of AI on our societies.
project aims at a more efficient and impactful use of intellectual capacity, infrastructure and financial resources, which are now separately streamlined to innovation and AI in all four countries.
The mission of the Institute is to improve policy making by means of supporting professional and open discussion among independent experts, government representatives and politicians.
Thereby it creates a living platform that identifies key political and economic challenges, analyzes their impact on society and offers evidence-based solutions.
The recommendations proposed as part of the project will help us and our partners to create more meaningful work agenda for the upcoming years.
- On 24. februar 2021
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