AI News, AI artificial intelligence
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 platforms, 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 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 rolling out recruiting chat bots that can automate repetitive communication tasks.
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.
Intelligence technologies enables coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).
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.
Beyond automation of writing tasks given data input, AI has shown significant potential for computers to engage in higher-level creative work.
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.
A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.
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.
Due to this high degree of complexity of the transportation, and in particular the automotive, application, it is in most cases not possible to train an AI algorithm in a real-world driving environment.
AI (Artificial Intelligence): What’s The Next Frontier For Healthcare?
Perhaps one of the biggest opportunities for AI (Artificial Intelligence) is the healthcare industry.According to ReportLinker, spending on this category is forecasted to jump from $2.1 billion to $36.1 billion by 2025.This is a hefty 50.2% compound annual growth rate (CAGR).
The aggregate of this data will be used to formulate predictive analytics to be used across diverse patient populations across the continuum of care, which will provide truly personalized medicine.
By combining genomics with individual patient data from electronic health records and real-world evidence on patient behavior culled from wearables, social media and elsewhere, health care providers can harness the power of precision medicine to determine the most effective approaches for specific patients.
AI can offer insights into a wealth of data to determine the likelihood of depression—based on the patient’s age, gender, comorbidities, genomics, life style, environment, etc.—and can provide information about potential reactions before they occur, thus enabling clinicians to provide more effective treatment sooner.
However, thanks to advances in advanced analytics and the vast amount of data available in life sciences today, we believe there is a real opportunity to transform the clinical trial process.
By leveraging patient-level data from historical clinical trials from Medidata's expansive clinical trial dataset, we can create a synthetic control arm (SCA) that precisely mimics the results of a traditional randomized control.
David Ferrucci: IBM Watson, Jeopardy Deep Conversations with AI | Artificial Intelligence Podcast
This conversation is part of the Artificial Intelligence podcast.INFO:Podcast website:https://lexfridman.com/aiiTunes:https://apple.co/2lwqZIrSpotify:https://spoti.fi/2nEwCF8RSS:https://lexfridman.com/category/ai/feed/Full episodes playlist:https://www.youtube.com/playlist?list...Clips playlist:https://www.youtube.com/playlist?list...EPISODE LINKS:Elemental Cognition Twitter: https://twitter.com/elementalcogElemental Cognition LinkedIn: https://www.linkedin.com/company/elem...David LinkedIn: https://www.linkedin.com/in/david-fer...OUTLINE:0:00 - Introduction1:06 - Biological vs computer systems8:03 - What is intelligence?31:49 - Knowledge frameworks52:02 - IBM Watson winning Jeopardy1:24:21 - Watson vs human difference in approach1:27:52 - Q&A vs dialogue1:35:22 - Humor1:41:33 - Good test of intelligence1:46:36 - AlphaZero, AlphaStar accomplishments1:51:29 - Explainability, induction, deduction in medical diagnosis1:59:34 - Grand challenges2:04:03 - Consciousness2:08:26 - Timeline for AGI2:13:55 - Embodied AI2:17:07 - Love and companionship2:18:06 - Concerns about AI2:21:56 - Discussion with AGICONNECT:- Subscribe to this YouTube channel- Twitter: https://twitter.com/lexfridman- LinkedIn: https://www.linkedin.com/in/lexfridman- Facebook: https://www.facebook.com/lexfridman- Instagram: https://www.instagram.com/lexfridman- Medium: https://medium.com/@lexfridman- Support on Patreon: https://www.patreon.com/lexfridman
- On 14. april 2021
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