The Artificial Intelligence is currently used as a term to describe the machines that can mimic functions of human beings that includes learning and solving of problems but was initially fostered on the idea of precisely describing the human intelligence and machines being able to simulate the same ( Nilsson, 2014) . Artificial Intelligence has stood out as the most controversial domains of inquiries in the field of computer science since it was coined in the 1950s ( Bench-Capon, 2007) . The concept and wholesome idea of AI originated in the private sector but the field grew both intellectually and in size of the community of research has become overwhelmingly dependent on investments in the public area. AI has since its coining seen significant advances that confirm its positive and untapped future.
Advances in AI
Many of the AI and algorithms of Machine Learning used today have been in existence for years. The advanced robots, UAVS and Autonomous vehicles have been used by many agencies of defence for approximately half a century ( Bench-Capon, 2007) . Many factors have been at work and a consensus stands that the recent developments like the massive improvements in Google translate, the victory of DeepMind at the game Go and natural conversational interface of the Alexa of Amazon ( Bench-Capon, 2007) .
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Machines and Humans
Machines have been for a long time been better than the brain of a human being at numerous kinds of tasks and more so those that relate to speed and scale of computation. According to Ajay et al., three academic economists, the recent advances in machine learning can be well classified as advances in the Machine “Prediction” ( Bench-Capon, 2007) . The completion of major tasks entails numerous components that include gathering of data, prediction, judgment and action. However, the humans continue to outpace the machines when it comes to tasks that entail judgment and Ajay et al. insist that the value of such tasks will increase with the reduction of cost of prediction with the machine learning.
The increase in frequency has ensured that the voice on the other end of the line is a computer ( Nilsson, 2014) . Four years have passed since Watson, the artificial intelligence program that I.B.M created beat the two best “Jeopardy” players in the world. Watson has access to at least 200 million pages of relevant information and data and manages to understand the queries of natural language as well as answer questions ( Bench-Capon, 2007) . The maker of the computer had it in mind to test the system as an expert adviser to the doctors which was an idea that the encyclopaedic knowledge that Watson had in the field of medical conditions furthered to assist a human expert in the diagnosis of diseases as well as contribute to the expertise of computers somewhere else in medicine. I.B.M went further to announce a version of the software that dealt with general purposes titled the “I.B.M ( Bench-Capon, 2007) . Watson Engagement Advisor” the idea is to ascertain the system of question-answering of the company that is available in a broad range of the call centre and technical support as well as applications for telephone sales.
Development of LISP
LISP has remained a very significant language of programming in research in AI and the history of the language shows the general benefits that come from the efforts of the researchers of AI in tackling the difficult problems. LISP in a similar manner to the other AI developments, shows how the AI researchers usually stretch the limits of computing technology and force new inventions in term of techniques in a bid to address the problems that come in representation and treatment of knowledge ( Bench-Capon, 2007) .
LISP has been very successful in the niche of commercial applications. For example, LISP is the scripting language used in AutoCAD that is a widely used program for Computer-aided design from Autodesk ( Stone, 2016) . The implementation of LISP required a good part of management of automatic memory. Therefore, it is well to appreciate that LISP has a critical influence and far beyond the AI in theory and designing of programming languages that entail all the programming languages that function well such as Java and SmallTalk ( Stone, 2016) .
Speech Recognition
The history that represents the system of speech recognition indicates the numerous themes that are common to AI research and the long periods of time between the initial research and the development of successful products as well as interactions between the researchers of AI and the wider community of researchers in the intelligence of machines ( Hager, 2017) .
The key capabilities behind the speech recognition today are derived from the early works of electrical engineers, statisticians and theorists of information as well as researchers of pattern-recognition ( Rastogi, 2016) . Another theme that is primary is the complementary nature of government and funding from the industry. Speech has grown from what was known in the year 1982 when the Dragon Systems founded by James and Janet Baker commercialized recognition of speech using technology ( Hager, 2017) . The graduate students at the Rockefeller University in the years 1970 became interested in recognition of speech as they observed the waveforms of speech on an oscilloscope.
Robotics
The navigation of robots in the static environments has been solved to a large proponent. The efforts today consider training a robot on how to interact with the world around the predictable and generalizable ways ( Hager, 2017) . Manipulation is the other topic of current interest that comes from the interactive environments. The revolution of deep learning has just began influencing the world of robots as it remains utterly difficult to acquire the large sets of data that drive other areas of AI that depend on learning ( Hager, 2017) . Reinforcement learning obviates the need for the labelled data that may assist in bridging the gap although it requires the system to be in a position to safely explore the space of policy without committing errors that harm it as well as others. The advances seen and made in the reliable perception of machines including the vision of computer, force and tactile perception will drive the machine learning and continue to be the essential enablers of advancing the robotic capabilities ( Hager, 2017) .
Computer Vision
Computer vision remains the most important form of machine perception. It has remained the sub-area of the Artificial Intelligence that has been transformed by a rise in the deep learning. The support vector machines were until a few years ago the preferred method of choice for a good part of the visual classification tasks ( Hager, 2017) . However, the confluence of the large-scale computing and more so the GPUs, availability of large sets of data and especially those available over the internet and refinements of the neural algorithms of work leads to a dramatic improvement in the benchmark and performance tasks as compared to when people are involved ( Rastogi, 2016) .
Future of AI
The overwhelming success of the paradigm driven by data has far displaced and is replacing the traditional paradigms of the AI. Procedures like proving of theorems and knowledge based on logic representation as well as reasoning receive reduced attention in part most likely because of the current challenge of connecting the groundings in the real world ( Markoff, 2013) . Planning that was the main characteristic of AI is also receiving less attention because it relies heavily on the assumptions of modelling that are difficult to satisfy in the realistic applications ( Markoff, 2013) . The approaches that depend on models like the ones that depend greatly on physics approach vision and traditional control and mapping in the robotics have been given way to the large data approaches that close the gap when sensing results of actions in the tasks that present themselves at hand.
The study panel experts expect an increasing focus on the development of systems over the next fifteen years. The systems will be human-aware and as such mean that they will be models specifically designed for characteristics of people with whom they are intended to interact ( Markoff, 2013) . A new interest has emerged and is one that seeks to find new and creative ways of developing the interactive and scalable means of teaching robots. In addition, the future proves to have a significant growth of new perceptions and recognition of objects in terms of capabilities and platforms of robots that are safe for humans ( Markoff, 2013). The growth will further extend to the products driven by data as well as the markets for these products.
It is also expected that there will be a re-emergence of certain traditional means of AI considering the practitioners have realized inevitable limitations that come with the entirely end-to-end approaches of deep learning ( Markoff, 2013) . The young researchers are thus encouraged to avoid reinventing the system but rather maintain an awareness of the key progress in many sectors of the AI during the first 50 years of field as well as in the related fields like the theory of control, psychology and cognitive science.
Conclusion
Artificial Intelligence is a term used to refer to an activity that commits to making machines intelligent while intelligence is that quality that ensures the entity to appropriately function in accordance with the conformity of its environment. AI has seen and has brought a good number of developments to the world today ranging from robots, language, computer systems and advanced transportation. The future is bright as there is an untapped portion of AI that needs intense research.
References
Bench-Capon, T. J., & Dunne, P. E. (2007). Argumentation in artificial intelligence: Artificial intelligence , 171 (10-15), 619-641.
Hager, G. D., Bryant, R., Horvitz, E., Mataric, M., & Honavar, V. (2017). Advances in Artificial Intelligence Require Progress Across all of Computer Science. ArXiv preprint arXiv: 1707.04352 .
Markoff, J. (2013). The Rapid Advance of Artificial Intelligence: Nytimes.com . Retrieved 2 December 2017, from http://www.nytimes.com/2013/10/15/technology/the-rapid-advance-of-artificial-intelligence.html?pagewanted=all
Nilsson, N. J. (2014). Principles of artificial intelligence : Morgan Kaufmann.
Rastogi, A. (2016). Artificial Intelligence — Human Augmentation is what’s here and now: CB Insights , 1.
Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., & Leyton-Brown, K. (2016). Artificial Intelligence and Life in 2030" One Hundred Year Study on Artificial Intelligence, Report of the 2015-2016 study panel.
