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The Legal Journal On Technology

AI 2.0: HUMANISING THE TECHNOLOGY

This Article has been written by Jyotsana Singh, A first year student at Symbiosis Law School, Hyderabad.

Introduction

In recent years, Artificial Intelligence (AI) has become the focus of the industry, media and political organisations, and the research and application of AI has grown rapidly both domestically and internationally. The industry is interested in knowing how AI is used. Through a large number of mergers and acquisitions and capital inflows, the development of AI technology is accelerating, which has strengthened the rapid transformation of the economies all over the world.[1] Technology developed in accordance with industry needs has led to major changes in the nature of services. For example, Microsoft’s Xiaoing chatbot transformed it from a traditional user interface to an interactive interface with natural language and emotional understanding. The social networking site LinkedIn was acquired by Microsoft in June 2016 to rebuild online communities with the help of artificial intelligence. The proliferation of equipment and technology provides great opportunities, but also great challenges. Balancing links through connections is not an easy task.[2] “The development of AI is driven both by research and by the information environment, with its accompanying social goals. Although both are very important, the latter always has the stronger driving force. With the current popularisation of the Internet, universal existence of sensors, emergence of big data, development of e-commerce, rise of the information community, and interconnection and fusion of data and knowledge in society, physical space, and cyberspace, the information environment surrounding AI development has changed profoundly, leading to a new evolutionary stage: AI 2.0. The emergence of new technologies also promotes AI to a new stage.”[3]

Historical background

Since its establishment, Artificial Intelligence has been continuously developing for more than 60 years, with major successes and failures. A review of the acquired knowledge should be able to assess AI trends. The "Father of Information Theory" at Bell Labs, IBM's N. Rochester and other researchers, in 1956 have defined AI.[4] Their definition of AI entails the ability of machines to understand, think and learn in the same way as humans, and shows that computers can imitate human understanding.[5] Since the 1970s, Artificial Intelligence research has expanded to include mechanical theorem proofs, machine translation, special systems, game theory, simulation, machine learning, robotics and intelligent control. These exploratory on-site processes have led to the development of numerous technologies and formed various schools of symbolism, relevance and behaviour. In 1950, British mathematician Alan Turing raised this question and published an article entitled "Computer Machines and Intelligence".[6] He also released a test, the Turing test, to determine whether the machines are intelligent. Some features of Turing test are:

◦ Natural language processing

◦ Knowledge representation

◦ Automated reasoning

◦ Machine learning

◦ Vision (For total Turing test)

◦ Motor Control (For total Turing test)

In addition to Turing, many scientists and mathematicians were also interested in this field. In 1956, the term "artificial intelligence" was proposed at a conference. Until 1980, governments of various countries have committed to developing it. However, due to lack of interest and slow progress, AI research was suspended after this period, but the work resumed again a few years later. Since then, artificial intelligence has come a long way.[7]

Advantages and disadvantages of AI technology

Advantages:

1. Human beings often make errors because of fatigue or carefree behaviour. However, these problems do not arise with AI software or machines. They therefore make fewer errors. They also learn from each iteration, which further increases their accuracy.[8]

2. AI systems can work faster than human teams. This makes them suitable for high-performance areas.

3. AI systems do not make any errors, as we said earlier. This makes them more trustworthy than others. You can count on them to work more accurately and to deliver the desired results.

4. In order to support various tasks, we use AI solutions. For instance, aircraft pilots use autopilot systems based on AI to help them fly their aircraft. Google Maps is another example that has great utility in everyday life. It uses AI to find the fastest routes between two locations and provides you with suggestions.

5. For many public services, AI can work excellently. It can help government organisations manage their data. Self-driving cars are a more advanced use of AI which can help to reduce traffic jams and accidents. Thus, we can apply AI to improve public services and it can also be used to provide services in many other areas.

Disadvantages:

1. Compared to other solutions, AI solutions are quite expensive. For an appropriate AI system, the hardware and software required are highly expensive. However, we can solve this problem by making more technological advances and developing cheaper solutions.

2. AI can be quicker and clever than people, but it can't figure out problems itself and come up with solutions "out of the box." For this reason, in most cases, human supervision is necessary. It just does what humans tell them to do. Thus, there is a huge scope for improvement in AI.[9]

3. As Al replaces humans with robots in doing most repetitive tasks and other such tasks, human intervention is decreasing, which creates serious problems in terms of employment standards.

4. All organizations strive to replace the least skilled people with artificial intelligence robots that can perform similar tasks which would give rise to unemployment.

Conclusion

Artificial intelligence is a very powerful and exciting field, and it will become more important and ubiquitous in the future, and will certainly continue to have a significant impact on modern society. In future, the most effective artificial intelligence tools will be developed and used to solve very complex problems.[10] Although the Terminator-like scenario is unlikely to appear in the short term, the advancement of Artificial Intelligence technologies and applications will continue. Artificial intelligence, deep learning, and neural networks are exciting and powerful technologies based on machine learning are used to solve many real-world problems. However, there is still a significant progress needed in the application of Artificial Intelligence technology and related algorithms.[11]

[1] Karl Regenbach, Live Tiles Ceo on AI: HUMANISING TECHNOLOGY IS THE FUTURE, https://livetilesglobal.com/livetiles-ceo-on-ai-humanising-technology-is-the-future/ (Last visited May 8, 2021). [2] The Official NVIDIA Blog, The Difference Between AI, Machine Learning, and Deep Learning?, https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence- machine-learning-deep-learning-ai/ (Last visited May 7, 2021). [3] ALEJANDRACAPEH, Artificial Intelligence: AI 2.0: The Humanizing of Machine Learning Technology – Pharmaceutical Executive, https://mindtalks.ai/ai-artificial-intelligence/mindtalks-artificial-intelligence-ai-2-0-the-humanizing-of-machine-learning-technology-pharmaceutical-executive-picked-by-mindtalks/, (Last visited Apr. 25, 2021). [4] Crevier D. AI: the tumultuous history of the search for artificial intelligence, New York: Basic Books, Inc.; 1993. [5] Yunhe Pan, Heading toward Artificial Intelligence 2.0, 2 Engineering 409, 413 (2016). [6] Supra 4. [7] Buchanan, Bruce G., A (Very) Brief History of Artificial Intelligence, AI Magazine 53, 60 (2015). [8] James Lynden, What does it means to Humanise technology? Being Human, https://becominghuman.ai/what-does-it-mean-to-humanise-tech-1c6c4f28bf91 (Last visited May 7, 2021). [9] Serigo Brodsky, To humanise Brand is to waste AI’s potential, marketingmag, https://www.marketingmag.com.au/hubs-c/opinion-brodsky-humanising-ai/ (Last visited May 7, 2021). [10] Brandon Purcell et al., AI 2.0: Upgrade Your Enterprise With Five Next-Generation AI Advances, Forrester, https://www.forrester.com/report/AI+20+Upgrade+Your+Enterprise+With+Five+NextGeneration+AI+Advances/-/E-RES163520 (Last visited May 8, 2021). [11] Castrounis A., Artificial Intelligence, Deep Learning, and Neural Networks, Explained, Kdnuggets, http://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural- networks-explained.html (Last visited May 8, 2021).

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