Assignment Sample on BUSI1602 Global Business and Sustainability
Introduction
There have been many new and different technologies emerging in market. It has highly transformed the human lives. Moreover, with change in time innovation is occurring in technology as well (Anderson and Luchsinger, 2018). The tech giants are investing a lot on RD to innovate new technology. Besides that, there are certain contemporary technologies exists such as AI, IOT, AR/VR and others. They all are being used by various companies. It has resulted in increasing efficiency of operations. There is a certain phase of every technology and its performance characteristics.
In this essay it will be described about the AI technology and its background. Also, it will be discussed about Anderson & Tushman’s technology cycle and S curve in technological improvement. Besides that, different adopters and innovations will be mentioned as well.
Background and evolution of technology
In present times, there are is constant technological advancement being occurred. It has resulted in emerging of new technology and software. Today, in each area there is use of technology which has made it easy for people and firms to perform tasks and attain goals. The industry is at boom that is expected to be $50 billion in next years (Bughin and Trench, 2017). Every tech giant is investing a lot of amounts in AI to expand their business.
Artificial intelligence is latest technology which studies the field of intelligence shown by machines in human nature. The intelligence shown is oppose to human behaviour. There are various types of applications in AI such as text speech, automates decision making system, self-driving cars and others. AI is not a new technology but it evolved since 19th century. In year 1943 there was a model proposed on artificial neurons by Warren and walter. In 1949 Donald Hebb formed a rule of Hebbian learning and in 1950 Alan turing did a test in order to test intelligent behaviour of human. The AI actually developed in 1955 when logic theorist was first AI program developed. There were 38 algorithms in it. In 1956 AI coin was used in academic field. Also, in 1972 in Japan first AI robot was designed that was Wabot 1. However, in 1980 expert system was developed based on decision making on human ability. In 2006 Facebook, Netflix started using AI and then slowly in 2011 deep learning was effectively used. This enabled in common use of AI by different companies.
It is found that there are different types of AI on basis of which they are used. The types of based on capability and based on functionality. Thus, on basis of capability there are weak, general and super AI and in functionality it is reactive, memory, theory of mind and self awareness. Hence, there is need to analyse features of AI so that they are used in proper way. There are several uses of AI. it is being used in online shopping where people need are recognized and products are shown to them (Acemoglu and Restrepo, 2019). It is being used in smart homes and cities in which robots are used to do some work. In every sector AI is used that is at airport to detect persons from thermal scanners. In cyber security AI helps to detect cyber threats and attacks. The data is recognized by AI software. In self driving cars AI is used.
The AI evolved in 1956 followed by new approach and funding. In a work shop in darthmed college the students began with developing teach machines to reason in accordance to perform sophisticated mental tasks like playing chess, proving mathematical theorems. Then, the US government spend millions in developing AI. However, in 21st century Moore’s law led to developing of advance algorithm. In 2007 Nvidia launched CUDA tool that allowed developers to write program to run on NVidia hardware. In 2009 AI researcher designed CPU for deep learning. In 2010 data access begins that is Kaggle, data science and other. Moreover, in 2011 machine learning began and in 2012 Google find out videos with help of deep learning and in 2015 open-source AI was used. In 2016 Pytorch released and tech companies like Amazon, Google started offering machine learning services (Collins and Moons, 2019). In 2018 fast AI google released tensor processing unit. So, till now there are changes occurring in AI.
Anderson & Tushman’s technology cycle
This cycle restart when the technology discontinues. So, it is mentioned as below:
Era of ferment– It is stage where technology development starts with new principle or discovery. The technology is not well known or understood. Alongside, its features and application are not known as well. The knowledge base is small and not structured in proper way. In this experiment is main activity which is done. Firstly, application is derived in which there is high variation. There is need of niche market in which more depth information of technology is derived. The era is small as knowledge is not proper. Also, there are high chances of failure in technological development. In this AI development started with new principle that is to recognize human nature. Also, with knowledge development and many of them predicted that a machine as intelligent as a human being would exist in no more than a generation.
Dominant design – It is identified with help of economy of scale and network externalities. In this knowledge become broader and expanded. However, there are various factors which influence on design as well. The knowledge structure changes and become in depth. Moreover, knowledge base gets increased as fields are from same principles and it enable in wide level of application for dominant design. The specialized and detailed knowledge about the core principles of the technology is relevant to increase performance and application opportunities to expand the number of possible adopters. In AI principles were same as to design human intelligence machine. So, various algorithms were designed for that (Davenport and Kalakota, 2019).
Era of incremental change– It is stage in which there are problems solved and technology performance is enhanced. Apart from it, the knowledge base is strong and principles are well understood in it. There are some political and social routines formed in as well. There may come new knowledge by which new ideas are integrated. Besides that, new features are added into technology that makes easy to make changes. The training is provided into this field in incremental change. Apart from AI, deep learning was developed that led to design of algorithms. This enhanced AI as new ideas was generated from that.
Technological discontinuity– This is last stage in which technology discontinues and there is no technical opportunities exist. Furthermore, if technology reach natural limit than there is change in customer preferences. Also, in this knowledge base is revitalized by disruption. However, new knowledge refresh technology in disruptive way. There new knowledge induce discontinuity. But if new knowledge is integrated with technology, then it results in redevelopment of new technology and similarly the entire cycle starts. In AI there do not exist any further advancement or opportunity that can be grabbed. There can be only programs added into it that improves efficiency of technology in future (Floridi, 2020).
It is evaluated that in AI as well this cycle is followed. In this present phase of AI is that it has not shifted towards machine learning. There is use of robotics in it by which advance development has occurred. Moreover, new algorithms are being formed for human behaviour. They consist of speech recognition, facial expression and many others.
Moreover, it is found that dominant design exists in AI as there is high scope of it. With new economies the network has also increased of AI. There are proper algorithms and structure in technology which is applied. There are several principles shared that allow in wide use of application of AI. There are some core principles set that increase performance to expand use of AI. Furthermore, dominant design will appear in 3-5 years when there will be new knowledge emerged. Apart from it, the design that will establish is specialized knowledge. In that advance algorithm is used in AI (Floridi, 2021).
S curve in Technological Improvement
It has been evaluated that S curve shows innovation from early stage as technology is developed to its steeper phase and then matures over time. So, from the curve it can be identified that how technology evolved. Also, it shows performance of technology by combined efforts of various actors such as company, university, institutes and other. In this curve there are 4 types of S curve that are mentioned as:
Level of invention– Here, any break through results in new industry. So, having a high solution enable to deliver an ideal way of delivering function. An example can be taken is to integrated circuits.
Number of invention– When any industry evolves there is decrease in its innovation in it. This is because of few opportunities and chance of innovation.
Performance– When innovation in technology occurs this results in steep rise but then gradually decreases. Therefore, in AI also its performance has decreased over time.
Profit – Similarly, in profit as well there is same situation occurs. The use of AI has led to decrease in profit.
The AI has changed a lot over time and majorly in 20th century. Since, start of 2010 there was only use of image net algorithms that used to detect and identify image (He and Zhang, 2019). There was no specific model used in it. In S curve there may occur disruptive innovation due to which old technology may overlap. It is said that technology is giving a lot of change to things
There are different characteristics of AI which has changed over time. The first is feature engineering in which minimal set of features is extracted from dataset. This enables in improving machine learning process. Besides, artificial neural network is feature that has evolved over time. In this each node calculate weight sum of value to input. The network used are CNN and RNN.
Another characteristic is IDRS that gather information and makes alert about any disaster to be incurred. The AI is used in to take relevant measure and actions. Similarly, deep learning is also characteristic of AI changed over time. This feature design computer GPU that helps in reducing processing time and giving useful outcomes. There are certain performance measures by which AI efficiency is measured (Vaishya and Haleem, 2020). The accuracy and efficacy of AI is measure that is used to measure. It clearly shows that how much accurate is AI outcomes are. In similar way, efficacy allows to measure effectiveness of algorithms and its implementation. These both allows to evaluate performance of AI. Thus, it has led to change technology over time.
It is determined that there were no discontinuities in AI which led to impacting on its efficiency (Holzinger and Müller, 2019). There were some inventions made into it like new algorithms, dominant design and other but it did not lead to add any value to AI. There was no new value added into market. The inventions done led to creating new applications for AI. For instance- voice and face recognition were innovation done in it. Hence, there is no impact on S curve as new innovation will not occur in it at add value to AI. Apart from it, the new S curve will be developed for new technology to be created. It will not be related to AI.
Different adopter
In technology there are various types of adopters which are classified as innovators, early adopter, majority, laggards and late majority. The adopters are those who have started to adopt technology at particular time period. When AI was developed there were various types of adopters who adopted this technology. Some at early stage and some very late. It is analysed that AI was adopted by technology firms that operate in technology sector. The companies were Apple, IBM, Microsoft and many others that belong to technology sector. For example- Apple adopted this in text speech voice of Alexa. It was done in voice recognition. Besides, IBM adopted AI to develop and form I algorithm.
It is identified that technology has not crossed transition. This is because the early majority have not gain market share even by adopting AI. They just have made use of technology by which it is easy for them to develop new algorithms (Jordan, 2019). In addition, even Apple has adopted AI but there is no transition occurred. The firm is able to use it in voice recognition. Besides, even early adopters are not having enough market share when technology was adopted.
Different innovations
In every technology there are some first movers and market leaders. The organisations adopt technology early and then gain market share. It is difficult to compete with them as firms have understood technology in depth and is now developing new algorithms. This is used in new applications and software emerging from that. Therefore, it is found that first mover in AI is Apple and IBM. They were firms that adopted this technology and made certain innovations in that. In that various AI models were being created and it led to gaining competitive advantage.
It is analysed that market leader in AI technology is Apple and Google. These 2 are top companies that led market. Besides, total revenue generated by them is $1 billion and market share are 26%. They all are leading market by using AI in developing its application, software and other things. In these most operations are performed by AI. Since 2012 to 16 there are almost 29 acquisitions being done by Apple in this field. It has enabled in making innovation in AI and machine learning. There are several new updates coming from it (Kudo and Mori, 2019).
The future of technology depends on how well it is being understood currently. Alongside, what is impact of that on economy and businesses. So, by analysing data the forecasting is done. It is necessary to gather relevant and accurate data so that AI future can be predicted in effective way. It is said that future of AI will be highly transformed and unpredictable. This is because there will be complete change in use of technology and AI. Moreover, entire human lives will be transformed due to it. The industry will be at boom that is expected to be $50 billion. Every tech giant will invest in AI to expand their business. So, the first thing is that AI will make work of humans easy and simple. The complex task will be done in effective way with use of AI. Also, there may be rise in unemployment as use of AI and robots will replace human beings. Hence, there will be no role of humans in performing job role.
Moreover, with further development in AI the complex task and processing work is done in effective and simple way. The AI will continue to drive innovation by which it potential areas of growth emerge. This will result in creating more jobs but will require experience and advance skills as well as capabilities for that. Apart from it, huge investment is made into it by companies which gives them platform to expand their business and gain competitive advantage. The focus of tech firms diverted towards R&D in which innovation occurs. But in future chances of failure of technology will increase (Lu and Serikawa, 2018). It is because new algorithms may not work in effective way and led to failure. With AI advances automation, few job roles will become obsolete. With no future for these roles, people will have to get new skills or training for new tasks. This will render a lot of people jobless, which can cause financial losses and economic issues.
AI will work completely like human nature. Besides, in decision making AI will play significant role. In practical tasks there will be more use of AI in it. Furthermore, in many sectors and areas AI will be highly utilized. For instance- in medical, in cybersecurity, in automobile and many other. Also, in nation defence system AI will be used to track movement of enemy. This will impact on country GDP growth in positive way as new technology will provide new tools and applications. AI will play an important role in e commerce in future, in every sector of industry from user experience to marketing to fulfilment and distribution. It is expected that AI will continue to drive e commerce , including through the use of chat-bots, shopper personalization to retain customers and fulfil their needs (Mak and Pichika, 2019). So, the future of AI is wide as in each sector there is use of it.
Conclusion
It can be summarized from essay that AI is technology that has emerged in 1956. There are different types of AI on basis of which they are used that is based on capability and based on functionality. In the model proposed by Anderson and Tuchman there are 4 stages of technology that is defined as era of ferment, emergence of dominant design, era of incremental and discontinuity. Moreover, it is found that dominant design exists in AI as there is high scope of it. Also, S curve shows innovation from early stage as technology is developed to its steeper phase and then matures over time. Also, different adopter of AI was Apple, IBM and Microsoft. The market leader in AI technology is Apple and Google. In future AI will transform entire human life.
References
Acemoglu, D. and Restrepo, P., (2019). 8. Artificial Intelligence, Automation, and Work (pp. 197-236). University of Chicago Press.
Anderson, J. and Luchsinger, A., (2018). Artificial intelligence and the future of humans. Pew Research Center, 10, p.12.
Bughin, J. and Trench, M., (2017). Artificial intelligence: The next digital frontier?.
Collins, G.S. and Moons, K.G., (2019). Reporting of artificial intelligence prediction models. The Lancet, 393(10181), pp.1577-1579.
Davenport, T. and Kalakota, R., (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), p.94.
Floridi, L., (2020). What the near future of artificial intelligence could be. In The 2019 Yearbook of the Digital Ethics Lab (pp. 127-142). Springer, Cham.
Floridi, L., (2021). Artificial intelligence, deepfakes and a future of ectypes. In Ethics, Governance, and Policies in Artificial Intelligence (pp. 307-312). Springer, Cham.
He, J. and Zhang, K., (2019). The practical implementation of artificial intelligence technologies in medicine. Nature medicine, 25(1), pp.30-36.
Holzinger, A. and Müller, H., (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), p.e1312.
Jordan, M.I., (2019). Artificial intelligence—the revolution hasn’t happened yet. Harvard Data Science Review, 1(1).
Kudo, S.E. and Mori, K., (2019). Artificial intelligence and colonoscopy: Current status and future perspectives. Digestive Endoscopy, 31(4), pp.363-371.
Lu, H.. and Serikawa, S., (2018). Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), pp.368-375.
Mak, K.K. and Pichika, M.R., (2019). Artificial intelligence in drug development: present status and future prospects. Drug discovery today, 24(3), pp.773-780.
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