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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 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.
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.
Surgical robots, new medicines and better care: 31 examples of AI in healthcare
Artificial intelligence simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. One of the world's highest-growth industries, the AI sector was valued at about $600 million in 2014 and is projected to reach a $150 billion by 2026.
Whether it's used to find new links between genetic codes or to drive surgery-assisting robots, artificial intelligence is reinventing — and reinvigorating — modern healthcare through machines that can predict, comprehend, learn and act.
The company’s deep learning platform analyzes unstructured medical data (radiology images, blood tests, EKGs, genomics, patient medical history) to give doctors better insight into a patient’s real-time needs.
The scientists used 25,000 images of blood samples to teach the machines how to search for bacteria. The machines then learned how to identify and predict harmful bacteria in blood with 95% accuracy.
Adam scoured billions of data points in public databases to hypothesize about the functions of 19 genes within yeast, predicting 9 new and accurate hypotheses.
BERG recently presented its findings on Parkinson’s Disease treatment — they used AI to find links between chemicals in the human body that were previously unknown — at the Neuroscience 2018 conference.
How it's using AI in healthcare: Combining AI, the cloud and quantum physics, XtalPi’s ID4 platform predicts the chemical and pharmaceutical properties of small-molecule candidates for drug design and development.
Additionally, the company claims its crystal structure prediction technology (aka polymorph prediction) predicts complex molecular systems within days rather than weeks or months.
Atomwise’s AI technology screens between 10 and 20 million genetic compounds each day and can reportedly deliver results 100 times faster than traditional pharmaceutical companies.
Location: London How it's using AI in healthcare: The primary goal of BenevolentAI is to get the right treatment to the right patients at the right time by using artificial intelligence to produce a better target selection and provide previously undiscovered insights through deep learning.
A 2016 study of 35,000 physician reviews revealed 96% of patient complaints are about lack of customer service, confusion over paperwork and negative front desk experiences.
The company’s technology helps hospitals and clinics manage patient data, clinical history and payment information by using predictive analytics to intervene at critical junctures in the patient care experience.
Location: Cleveland, Ohio How it's using AI in healthcare: The Cleveland Clinic teamed up with IBM to infuse its IT capabilities with artificial intelligence. The world-renowned hospital is using AI to gather information on trillions of administrative and health record data points to streamline the patient experience.
Since implementing the program, the facility has seen a 60% improvement in its ability to admit patients and a 21% increase in patient discharges before noon, resulting in a faster, more positive patient experience.
Additionally, the inability to connect important data points is slows the development of new drugs, preventative medicine and proper diagnosis. Many in healthcare are turning to artificial intelligence as way to stop the data hemorrhaging.
Location: Seattle How it's using AI in healthcare: KenSci combines big data and artificial intelligence to predict clinical, financial and operational risk by taking data from existing sources to foretell everything from who might get sick to what's driving up a hospital’s healthcare costs.
The company’s software helps pathology labs eliminate bottlenecks in data management and uses AI-powered image analysis to connect data points that support cancer discovery and treatment.
How it's using AI in healthcare: When IBM’s Watson isn’t competing on Jeopardy!, it's helping healthcare professionals harness their data to optimize hospital efficiency, better engage with patients and improve treatment.
Location: Shenzhen, China How it's using AI in healthcare: ICarbonX is using AI and big data to look more closely at human life characteristics in a way they describe as “digital life.' By analyzing the health and actions of human beings in a “carbon cloud,' the company hopes its big data will become so powerful that it can manage all aspects of health.
Robots equipped with cameras, mechanical arms and surgical instruments augment the experience, skill and knowledge of doctors to create a new kind of surgery. Surgeons control the mechanical arms while seated at a computer console while the robot gives the doctor a three dimensional, magnified view of the surgical site that surgeons could not get from relying on their eyes alone.
Being the first robotic surgery assistant approved by the FDA over 18 years ago, the surgical machines feature cameras, robotic arms and surgical tools to aide in minimally invasive procedures.
Under a physician’s control, the tiny robot enters the chest through a small incision, navigates to certain locations of the heart by itself, adheres to the surface of the heart and administers therapy.
Location: Eindhoven, The Netherlands How it's using AI in healthcare: MicroSure’s robots help surgeons overcome their human physical limitations. The company's motion stabilizer system reportedly improves performance and precision during surgical procedures.
Location: Caesarea, Israel How it's using AI in healthcare: Surgeons use the Mazor Robotics' 3D tools to visualize their surgical plans, read images with AI that recognizes anatomical features and perform a more stable and precise spinal operation.
Artificial Intelligence is the great healthcare equalizer
Receiving high-quality diagnostic images for review anywhere in the world, for example, is possible due to virtual radiology as AI algorithms can be supported in the cloud, speeding workflows and rapidly consolidating data.
Last year, in a major breakthrough, AI showed how it can help identify patients at highest risk for developing sepsis, which often requires intensive analysis of highly complex data sets.
In North America and Europe, a ‘hub and spoke’ model for healthcare systems is emerging, through which care is delivered primarily through ambulatory channels and outpatient clinics with a focus on better prevention and out-of-hospital care, reserving hospital visits for complex care cases.
AI can collate data from the clinics to a centralized war room, connect remote doctors with expertise from hospitals and determine the right location of clinics and service mix based on community needs.
Machine learning can find patterns in patient admission and discharges to determine which patient categories tend to overstay in hospitals -- a major expense for providers -- and reduce patients’ stays accordingly.
By finding patterns based on large subsets of data, AI lends greater precision to segmenting patients based on risk levels and identifying a proactive course of action to treat those deemed high-risk patients.
- On Tuesday, April 23, 2019
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