FIFTH GENERATION COMPUTERS
The period of Fifth Generation is 1980-till date.
In the fifth generation, the VLSI technology became ULSI (Ultra Large Scale Integration) technology, resulting in the production of microprocessor chips having ten million electronic components.
This generation is based on parallel processing hardware and AI (Artificial Intelligence) software.
AI is an emerging branch in computer science which interprets means and methods of making computers think like human beings.
All the higher level languages like C and C++, Java, .Net, etc., are used in this generation.
Fifth generation computers are in developmental stage which is based on the artificial intelligence. The goal of the fifth generation is to develop the device which could respond to natural language input and are capable of learning and self-organization. Quantum computation and molecular and nanotechnology will be used in this technology. So we can say that the fifth generation computers will have the power of human intelligence.
AI includes:
- Robotics
- Neural networks
- Game Playing
- Development of expert systems to make decisions in real life situations.
- Natural language understanding and generation.
The main features of Fifth Generation are:
- ULSI technology
- Development of true artificial intelligence
- Development of Natural language processing
- Advancement in Parallel Processing
- Advancement in Superconductor technology
- More user friendly interfaces with multimedia features
- Availability of very powerful and compact computers at cheaper rates
Some computers types of this generation are:
- Desktop
- Laptop
- NoteBook
- UltraBook
- ChromeBook
CHARACTERISTICS
1) The fifth generation computers will use super large scale integrated chips.
2) They will have artificial intelligence.
3) They will be able to recognize image and graphs.
4) Fifth generation computer aims to be able to solve highly complex problem including decision making, logical reasoning.
5) They will be able to use more than one CPU for faster processing speed.
6) Fifth generation computers are intended to work with natural language.
Advantages
The goal of knowledge-based systems is to make the critical information required for the system to work explicit rather than implicit. In a traditional computer program the logic is embedded in code that can typically only be reviewed by an IT specialist. With an expert system the goal was to specify the rules in a format that was intuitive and easily understood, reviewed, and even edited by domain experts rather than IT experts. The benefits of this explicit knowledge representation were rapid development and ease of maintenance.
Ease of maintenance is the most obvious benefit. This was achieved in two ways. First, by removing the need to write conventional code many of the normal problems that can be caused by even small changes to a system could be avoided with expert systems. Essentially, the logical flow of the program (at least at the highest level) was simply a given for the system, simply invoke the inference engine. This also was a reason for the second benefit: rapid prototyping. With an expert system shell it was possible to enter a few rules and have a prototype developed in days rather than the months or year typically associated with complex IT projects.
A claim for expert system shells that was often made was that they removed the need for trained programmers and that experts could develop systems themselves. In reality this was seldom if ever true. While the rules for an expert system were more comprehensible than typical computer code they still had a formal syntax where a misplaced comma or other character could cause havoc as with any other computer language. In addition as expert systems moved from prototypes in the lab to deployment in the business world issues of integration and maintenance became far more critical. Inevitably demands to integrate with and take advantage of large legacy databases and systems arose. To accomplish this integration required the same skills as any other type of system.
Disadvantages
The most common disadvantage cited for expert systems in the academic literature is the knowledge engineering problem. Obtaining the time of domain experts for any software application is always difficult but for expert systems it was especially difficult because the experts were by definition highly valued and in constant demand by the organization. As a result of this problem a great deal of research effort in the later years of expert systems was focused on tools for knowledge acquisition, to help automate the process of designing, debugging, and maintaining rules defined by experts. However, when looking at the life-cycle of expert systems in actual use other problems seem at least as critical as knowledge acquisition. These problems with expert systems were essentially the same problems as any other large system: integration, access to large databases, and performance.
Performance was especially problematic for early expert systems as they were built using tools that featured interpreted rather than compiled code such as Lisp. Interpreting provides an extremely powerful development environment but with a cost that it is virtually impossible to obtain the levels of efficiency of the fastest compiled languages of the time such as C. System and database integration were difficult for early expert systems due to the fact that the tools were mostly in languages and platforms that were not familiar to nor welcomed in most corporate IT environments. Programming languages such as Lisp and Prolog and hardware platforms such as Lisp Machines and personal computers. As a result a great deal of effort in the later stages of expert system tool development were focused on integration with legacy environments such as COBOL, integration with large database systems, and porting to more standard platforms. These issues were resolved primarily by the client-server paradigm shift as PCs were gradually accepted in the IT world as a legitimate platform for serious business system development and as affordable minicomputer servers provided the processing power needed for AI applications.
