Down to the Wire: A Short Introduction to Artificial Intelligence
For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness. At Stanford, researchers are using AI to analyze satellite images to identify which areas have the highest poverty levels. In agriculture new AI advancements show improvements in gaining yield and to increase the research and development of growing crops. New artificial intelligence now predicts the time it takes for a crop like a tomato to be ripe and ready for picking thus increasing efficiency of farming.
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. More specializations of Ai in agriculture is one such as greenhouse automation , simulation , modeling , and optimization techniques. More and more of the public perceives that the adaption of these new techniques and the use of Artificial intelligence will help reach that goal.
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 use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplane simulators are using artificial intelligence in order to process the data taken from simulated flights.
Other than simulated flying, there is also simulated aircraft warfare. The computers are able to come up with the best success scenarios in these situations. The computers can also create strategies based on the placement, size, speed and strength of the forces and counter forces.
Pilots may be given assistance in the air during combat by computers. 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. Multiple aircraft are needed to get good approximations for some calculations so computer simulated pilots are used to gather data. It is a rule based expert system put together by collecting information from TF documents and the expert advice from mechanics that work on the TF The performance system was also used to replace specialized workers.
The system allowed the regular workers to communicate with the system and avoid mistakes, miscalculations, or having to speak to one of the specialized workers. The AOD also uses artificial intelligence in speech recognition software. The air traffic controllers are giving directions to the artificial pilots and the AOD wants to the pilots to respond to the ATC's with simple responses. The programs that incorporate the speech software must be trained, which means they use neural networks. The program used, the Verbex , is still a very early program that has plenty of room for improvement.
The improvements are imperative because ATCs use very specific dialog and the software needs to be able to communicate correctly and promptly every time. The Artificial Intelligence supported Design of Aircraft,  or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process.
The software also allows the user to focus less on the software tools. The AIDA uses rule based systems to compute its data. This is a diagram of the arrangement of the AIDA modules.
Reasons for optimism
Although simple, the program is proving effective. In , NASA 's Dryden Flight Research Center , and many other companies, created software that could enable a damaged aircraft to continue flight until a safe landing zone can be reached. The neural network used in the software proved to be effective and marked a triumph for artificial intelligence. The Integrated Vehicle Health Management system, also used by NASA, on board an aircraft must process and interpret data taken from the various sensors on the aircraft.
The system needs to be able to determine the structural integrity of the aircraft. The system also needs to implement protocols in case of any damage taken the vehicle. 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.
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.
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AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered a part of AI. See AI effect. AI can be used to potentially determine the developer of anonymous binaries. AI can be used to create other AI. In June , a research team from the visual computing group of the Technical University of Munich and from Stanford University developed Face2Face,  a program which animates the face of a target person, transposing the facial expressions of an exterior source.
The technology has been demonstrated animating the lips of people including Barack Obama and Vladimir Putin. Since then, other methods have been demonstrated based on deep neural network , from which the name " deepfake " was taken.
AI: Equally Disrupting and Enabling
Hollywood film studios had already used the technique in animated films, [ which? The main difference is that today anyone can use a deep fake software and rig videos. In September , the U. Senator Mark Warner proposed to penalize social media companies that allow sharing of deepfake documents on their platform. Vincent Nozick, a researcher from the Institut Gaspard Monge , found a way to detect rigged documents by analyzing the movements of the eyelid.
Department of Defense has given 68 million dollars to work on deepfake detection.cloudmedicaltranscription.com/components/appanoose/10771.php
Connecting artificial intelligence with the internet of things
The future of AI in the classroom is looking bright. AI tutors also eliminate the intimidating idea of tutor labs or human tutors which can cause anxiety and stress for some students. Ambient informatics is the idea that information is everywhere in the environment and that technologies automatically adjust to your personal preferences.
While there are many benefits to the use of AI in the classroom, there are also several dangers that need to be taken into account before implementing them. Another advancement includes the presentation of performance data and enrichment methods on an individual basis. Within curriculum, AI could help determine if there are underlying biases in texts and instructions. For teachers, AI could soon have the power to relay data regarding efficacy of varying learning interventions from a, potentially, global database. 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.
Although AI can provide many assets to a classroom, many experts still agree that they will not be able to replace teachers altogether. Many teachers fear the idea of AI replacing them in the classroom, especially with the idea of personal AI assistants for each student. The reality is, AI can create a more dystopian environment with revenge effects. This means that technology is inhibiting society from moving forward and causing negative, unintended effects on society.
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. Such trading is called High-frequency Trading , and it represents one of the fastest growing sectors in financial trading.
Many banks, funds, and proprietary trading firms now have entire portfolios which are managed purely by AI systems. 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.
Several large financial institutions have invested in AI engines to assist with their investment practices. Its wide range of functionalities includes the use of natural language processing to read text such as news, broker reports, and social media feeds. It then gauges the sentiment on the companies mentioned and assigns a score. Its machine learning systems mine through hoards of data on the web and assess correlations between world events and their impact on asset prices.
Several products are emerging that utilize AI to assist people with their personal finances. 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.
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. Robo-advisors are becoming more widely used in the investment management industry. Robo-advisors provide financial advice and portfolio management with minimal human intervention.
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. It can adjust to real-time changes in the market and accordingly calibrate the portfolio. Their technology will be licensed to banks for them to leverage for their underwriting processes as well. 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 platform is particularly useful to assign credit scores to those with limited credit histories, such as millennials. A comprehensive reference for all the AI topics that we will cover. Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks this is the textbook for CS Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning. Available free online. Hastie, Tibshirani, and Friedman. The elements of statistical learning. Covers machine learning.
Foundations of constraint satisfaction. Covers constraint satisfaction problems. Bear in mind that some of these books can be quite dense and use different notation terminology, so it might take some effort to connect up with the material from class. Open package e. Each homework is centered around an application and will also deepen your understanding of the theoretical concepts. Some homeworks will have a competition component; winners will receive extra credit. All assignments are due at 3pm on the due date 30 minutes before class. Here are all the homework deadlines:.
Written assignments: Homeworks should be written up clearly and succinctly; you may lose points if your answers are unclear or unnecessarily complicated. Here is an example of what we are looking for. You are encouraged to use LaTeX to writeup your homeworks here's a template , but this is not a requirement. Programming assignments: The grader runs on Python 2.
Please use Python 2. Electronic Submission: All assignments are due at 3pm on the due date. Assignments are submitted through Gradescope. If you need to sign up for a Gradescope account, please use your stanford. You can submit as many times as you'd like until the deadline: we will only grade the last submission. Submit early to make sure your submission runs properly on the Gradescope servers. If anything goes wrong, please ask a question on Piazza or contact a course assistant. Do not email us your submission. To explore this issue, I have considered the capabilities of the vertebrate retina, which is understood well enough to serve as a Rosetta stone roughly relating nervous tissue to computation.
By comparing how fast the neural circuits in the retina perform image-processing operations with how many instructions per second it takes a computer to accomplish similar work, I believe it is possible to at least coarsely estimate the information-processing power of nervous tissue—and by extrapolation, that of the entire human nervous system. The human retina is a patch of nervous tissue in the back of the eyeball half a millimeter thick and approximately two centimeters across. It consists mostly of light-sensing cells, but one tenth of a millimeter of its thickness is populated by image-processing circuitry that is capable of detecting edges boundaries between light and dark and motion for about a million tiny image regions.
Each of these regions is associated with its own ber in the optic nerve, and each performs about 10 detections of an edge or a motion each second. The results ow deeper into the brain along the associated ber. From long experience working on robot vision systems, I know that similar edge or motion detection, if performed by efcient software, requires the execution of at least computer instructions. The entire human brain is about 75, times heavier than the 0. Personal computers in are just about a match for the 0.
Brainpower and Utility Though dispiriting to articial-intelligence experts, the huge decit does not mean that the goal of a humanlike articial brain is unreachable. Computer power for a given price doubled each year in the s, after doubling every 18 months in the s and every two years before that. Prior to this progress made possible a great decrease in the cost and size of robot-controlling computers. Cost went from many millions of dollars to a few thousand, and size went from room-lling to handheld.
Power, meanwhile, held steady at about 1 MIPS. Since cost and size reductions have abated, but power has risen to about 10, MIPS for a home computer. At the present pace, only about 20 or 30 years will be needed to close the gap. Commercial and research experiences convince me that the mental power of a guppy—about 10, MIPS—will sufce to guide mobile utility robots reliably through unfamiliar surroundings, suiting them for jobs in hundreds of thousands of industrial locations and eventually hundreds of millions of homes.
Commercial mobile robots have found few jobs. A paltry 10, work worldwide, and the companies that made them are struggling or defunct. Makers of robot manipulators are not doing much better.
The AI Revolution: Our Immortality or Extinction
The largest class of commercial mobile robots, known as automatic guided vehicles AGVs , transport materials in factories and warehouses. Most follow buried signal-emitting wires and detect end points and collisions with switches, a technique developed in the s. It costs hundreds of thousands of dollars to install guide wires under concrete oors, and the routes are then xed, making the robots economical only for large, exceptionally stable factories. Some robots made possible by the advent of microprocessors in the s track softer cues, like magnets or optical patterns in tiled oors, and use ultrasonics and infrared proximity sensors to detect and negotiate their way around obstacles.
The most advanced industrial mobile robots, developed since the late s, are guided by occasional navigational markers—for instance, laser-sensed bar codes—and by preexisting features such as walls, corners and doorways. The costly labor of laying guide wires is replaced by custom software that is carefully tuned for each route segment.
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The small companies that developed the robots discovered many industrial customers eager to automate transport, oor cleaning, security patrol and other routine jobs. Alas, most buyers lost interest as they realized that installation and route changing required time-consuming and expensive work by experienced route programmers of inconsistent availability.
Technically successful, the robots zzled commercially. In failure, however, they revealed the essentials for success. First, the physical vehicles for various jobs must be reasonably priced. Fortunately, existing AGVs, forklift trucks, oor scrubbers and other industrial machines designed for accommodating human riders or for following guide wires can be adapted for autonomy.
Second, the customer should not have to call in specialists to put a robot to work or to change its routine; oor cleaning and other mundane tasks cannot bear the cost, time and uncertainty of expert installation. Third, the robots must work reliably for at least six months before encountering a problem or a situation requiring downtime for reprogramming or other alterations.
Six months, though, earned the machines a sick day. Robots exist that have worked faultlessly for years, perfected by an iterative process that xes the most frequent failures, revealing successively rarer problems that are corrected in turn. Unfortunately, that kind of reliability has been achieved only for prearranged routes. Such robots are easily confused by minor surprises such as shifted bar codes or blocked corridors not unlike ants thrown off a scent trail or a moth that has mistaken a streetlight for the moon.
A Sense of Space Robots that chart their own routes emerged from laboratories worldwide in the mids, as microprocessors reached MIPS. Of course, they still fall far short of the six-month commercial criterion. Too often different locations in the coarse maps resemble one another. Conversely, the same location, scanned at different heights, looks different, or small obstacles or awkward protrusions are overlooked.
But sensors, computers and techniques are improving, and success is in sight. My efforts are in the race.
In the s at Carnegie Mellon we devised a way to distill large amounts of noisy sensor data into reliable maps by accumulating statistical evidence of emptiness or occupancy in each cell of a grid representing the surroundings. The approach worked well in two dimensions and still guides many of the robots described above.
Three-dimensional maps, 1, times richer, promised to be much better but for years seemed computationally out of reach. In we used economies of scale and other tricks to reduce the computational costs of three-dimensional maps fold. Continued research led us to found a company, Seegrid, that sold its first dozen robots by late Robot, Version 1.
A few thousand visually distinctive patches in the surroundings are selected in each glimpse, and their 3-D positions are statistically estimated.