“We need to stop providing chip makers with more data,” said Ting Ku, senior director of engineering for Nvidia, in a Design Automation Conference panel last month. “We need tools to make some decisions.”
Machine Learning Offers Helping Hand To Edit Chips
Tasked with squeezing billions of transistors onto fingernail-sized slabs of silicon, chip designers are asking whether machine learning can help.
In the view of electronic design automation firms, machine learning tools could chisel rough edges off complex chips, improving productivity, optimizing trade-offs like power consumption and timing, and testing that chips are ready for manufacturing.
Though chip design is still a deeply creative process, engineers need tools that abstract the massive number of variables in modern chips. Using statistics, the software generates models fitted to simulations that replicate how physical chips will work. The tools would seem to be prime candidates for machine learning, which can be trained to find hidden insights in data without explicit programming.
But these teachable tools are still rare, said Elyse Rosenbaum, a professor of electrical and computer engineering at the University of Illinois Urbana-Champaign, in a telephone interview. Most machine learning tools that do exist are used to confirm that chips match specifications and will be manufactured without flaws.
Rosenbaum, who helps lead the Center for Advancing Electronics with Machine Learning (CAEML), said that most EDA applications will require humans for the training side of the equation. That contrasts from image recognition and cancer detection programs, which excel with unsupervised forms of machine learning.
Designing chips creates lots of data – and sometimes more than engineers know what to do with. “We need to stop providing chip makers with more data,” Ting Ku said on a panel at the Design Automation Conference last month in Austin, Texas. “We need tools to make some decisions.”
Ku, senior director of engineer for Nvidia, said that the company is already using machine learning to provide insights into manufacturing variations that could affect its graphics chips. And these variations are growing more unpredictable with the shift toward smaller process nodes like 10 nanometers.
But this is still virgin ground for an industry that only a few years ago signed onto big data analytics. “We can smell machine learning problems, but we can’t just take a course in it,” said Jeff Dyck, vice president of technical operations at Solido Design Automation. “We cannot back designs on guesses, we need higher levels of confidence.”
Solido’s tools are representative of how the industry is dipping its toes into machine learning. The firm recently released new characterization tools, which after being trained on circuit simulations can make faster predictions than other tools about how, for instance, standard cells and memory will react to higher-than-normal voltages.
Amit Gupta, Solido’s chief executive, said in an email that the software “automatically determines and runs specific simulations, that is used as the training data to build the machine learning models in real time. The models then predict results with brute force accuracy. We find that building design-specific models per run is effective.”
Solido claims that its other tools can verify memory, analog, and other circuits against statistical process variation faster than conventional software. Solido, which recently started a program called ML Labs to build special tools for customers, says that more than 40 companies use the variation-aware design tools to cut power consumption and die size.
Lots of other possibilities exist, though. Dave Kelf, vice president of marketing for OneSpin Solutions, said in an interview that the company is looking to apply machine learning to formal verification, which uses statistics to locate errors missed by simulations. Manish Pandey, Synopsys’ chief architect for new technologies, has floated the same concept.
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Because of the incredible flexibility of the artificial intelligence process, AI software can be tailored to any problem requiring its special qualities. For example, there are all kind of problems with no algorithmic solution. Non-numerical problems often don’t yield to the algorithmic process. Neither do problems with uncertainty and ambiguity, but both are readily solved with AI techniques. With algorithmic software, the problem is guaranteed to be solved; with AI, there can be partial or even no solution. As a result, AI often fits the disorganized, imperfect real world better than conventional software because it can deal with shades of gray.
The first applications of AI were games playing and puzzles solving. Researchers felt that the ability of a computer to play a game was an excellent demonstration of human intelligence, and it is, in a controlled and limited way. AI programs to play chess, checkers, backgammon, tic-tac-toe and others were written, tested and improved. AI programs were also written to solve puzzles and riddles. The first applications proved the symbolic processing concepts of AI, and games and puzzles are still used as examples in teaching.
However, AI researchers discovered limitations of computers in initial applications of AI. Huge memory and processing power was needed to store the knowledge base. Fortunately, this problem was solved by recent development of Computer technology. Today, mini-computers routinely run AI software.
Although games are interesting, they are not particularly useful. Computer users are looking for programs that will increase productivity and performance, and artificial intelligence has found some specific niches in which it excels. These applications are given in below picture and some of them are discussed in detail as the article progresses.
General Problem Solving
General problem solving is something AI does well, and that ability can be applied to a very wide range of subjects. For people with tough problems to solve or problems that don’t fit algorithmic processes, AI offers a fresh alternative.
Some early attempts were made to create a general purpose AI problem-solving program. Although limited success was achieved, the approach was just not suitable for all types and sizes of problems. As a result, virtually all AI software is custom designed for the application. The best AI software is designed and optimized for the specific job.
In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
Understanding Natural Language
Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.
The world is composed of three-dimensional objects, but the inputs to the human eye and computers’ TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.
One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).
Fuzzy logic controllers have been developed for automatic gearboxes in automobiles. For example, the 2006 Audi TT, VW Touareg and VW Caravell feature the DSP transmission which utilizes Fuzzy Logic. A number of Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic-based controller.
Today’s cars now have AI-based driver assist features such as self-parking and advanced cruise controls. AI has been used to optimize traffic management applications, which in turn reduces wait times, energy use, and emissions by as much as 25 percent. In the future, fully autonomous cars will be developed. AI in transportation is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. 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.
Various tools of artificial intelligence are also being widely deployed in homeland security, speech and text recognition, data mining, and e-mail spam filtering. Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition, global voice recognition (from a variety of people in a noisy room), and facial expression recognition for interpretation of emotion and non-verbal cues. Other applications are robot navigation, obstacle avoidance, and object recognition.
Complete List of applications
Typical problems to which AI methods are applied
- Optical character recognition
- Handwriting recognition
- Speech recognition
- Face recognition
- Artificial creativity
- Computer vision, Virtual reality and Image processing
- Diagnosis (artificial intelligence)
- Game theory and Strategic planning
- Game artificial intelligence and Computer game bot
- Natural language processing, Translation and Chatterbots
- Nonlinear control and Robotics
Other fields in which AI methods are implemented
- Artificial life
- Automated reasoning
- Biologically inspired computing
- Concept mining
- Data mining
- Knowledge representation
- Semantic Web
- E-mail spam filtering
- Behavior-based robotics
- Developmental robotics (Epigenetic)
- Evolutionary robotics
- Hybrid intelligent system
- Intelligent agent
- Intelligent control
- Crash course in AI and expert systems by Louis E. Frenzel, Jr
- image intelligent transport system
According to Kernel founder Bryan Johnson: “The market for implantable neural prosthetics including cognitive enhancement and treatment of neurological dysfunction will likely be one of if not the largest industrial sectors in history.”
© 2017 Wall Street Daily, LLC
What is Human Intelligence
Human intelligence is something natural, no artificiality is involved in it. In all fields, intelligence is something differently perceived and differently acquired. More specifically, human intelligence is something related to the adaption of various other cognitive process in order to have specific environment. In human intelligence, the word ”intelligence” plays a vital role because intelligence is with them all it’s need to cogitate and make a step by step plan for performing certain task. It’s a natural blessing that is with humans since their birth and no one can replace it except GOD.
What is Artificial Intelligence
Artificial intelligence is designed to add human like qualities in robotic machines. Its major function is to make the robots a good mimic of human beings. In short we can say that it’s basically working to make robots a good of copier of humans. Researchers are all time busy in making up a mind that can behave like a human mind, they are putting efforts in doing this task now a days.Weak AI is the thinking focused towards the development of technology gifted of carrying out pre-planned moves based on some rules and applying these to achieve a certain goal. Strong AI is emerging technology that can think and function same as like humans, not just imitating human behavior in a certain area.
“Computers will overtake humans with AI at some within the next 100 years. When that happens, we need to make sure the computers have goals aligned with ours.”
As Ars Technica U.K.’s Sebastian Anthony reported on October 28, 2016:
Google Brain has created two artificial intelligences that evolved their own cryptographic algorithm to protect their messages from a third AI, which was trying to evolve its own method to crack the AI-generated crypto. The study was a success: The first two AIs learnt how to communicate securely from scratch.
© 2017 Wall Street Daily, LLC
Artificial Intelligence vs Human Intelligence
What’s actual difference underlying? By combining both definitions from the tunnel of technology we can say that human intelligence works naturally and make up a certain thought by adding different cognitive processes. On the other hand, Artificial intelligence are on the way to make up a model that can behave like humans which seems impossible because nothing can replace a natural thing into an artificial thing.
Major differences surely lies in!
- Human intelligence is analogue as work in the form of signals and artificial intelligence is digital, they majorly works in the form of numbers.
- Humans uses heir schema and content memory whereas as is using the built in , designed by scientist memory.
- There is a distinction in hardware and software thing in human working mind. Their intelligence is not based on these issues.
- Human brain does have body but their brain is no body.
- Last but the most important difference we can gather up is that human intelligence is bigger and artificial intelligence as the name suggests is artificial, little and temporary.
- HI is reliable whereas AI is not.Although there are people who argues that Humans makes more mistakes as compared to AI.
Concluding the whole discussion, we can sum up that both have a huge difference between each other, one is natural then other is completely opposite. Scientists are trying hard to come up at high level but one thing is clear that what GOD has made no one else can replace it.
© 2017 Wall Street Daily, LLC
Difference between Artificial Intelligence and Human Intelligence