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AI is going to change the world more than anything in the history of mankind.<\/span><\/p>\n – Kai-Fu Lee<\/span><\/p>\n <\/p>\n \u00a0<\/strong><\/span>Artificial Intelligence (AI) has permeated into all aspects of our lives. <\/span>AI tools are everywhere we look. <\/span>AI is\u00a0 delivering tangible benefits across every \u00a0industry one can think about.<\/span><\/span><\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n Future Trajectory<\/span><\/strong><\/span><\/p>\n Most AI applications today are classified as \u201cnarrow\u201d or \u201cweak\u201d AI, meaning that they usually carry out a specific task they are designed for. \u00a0<\/span><\/p>\n AI is only just getting started.\u00a0<\/span>Computers will get smarter, quicker, and increasingly become capable of tasks that traditionally are carried out by humans, such as making complex decisions or engaging in creative thought. <\/span>Truly intelligent entities would not be designed for one specific task but would be able to carry out many number of tasks.<\/span><\/span><\/p>\n \u00a0<\/strong><\/span>Computing power is the engine of AI.<\/span><\/p>\n Quantum computing, along with other next-level processing capabilities such as biological and neuromorphic computing, is likely to unlock even more possibilities.<\/span><\/p>\n Quantum computing is basically, ability of sub-atomic particles to exist in more than one state at the same time. It is theoretically capable of completing some calculations up to 100 trillion times faster <\/strong>than today\u2019s fastest computers.<\/span><\/p>\n In order to continually evolve to become smarter, machine learning models will inevitably become larger.<\/span><\/p>\n \u00a0Additionally, more processing power means we will be able to create larger amounts of \u201csynthetic\u201d data for training purposes, reducing the need for collecting real data to feed into algorithms for many applications.<\/span><\/p>\n Other technologies like neuromorphic computing would be able to mimic the \u201celastic\u201d capabilities of the human brain to adapt themselves to processing new forms of information.<\/span><\/p>\n \u00a0<\/strong><\/span>These days we can see art, music, poetry, and even computer code is being created by AI.<\/span><\/p>\n This has been made possible by the ongoing development of \u201cgenerative\u201d AI i.e. when Ai creates new data rather than simply analyzing and understanding existing data.<\/span><\/p>\n With generative AI, analyzing and understanding is the first step of the process. It then takes what it has learned and uses it to build further examples of the models that it has studied.<\/span><\/p>\n This ability to create synthetic data will lead us into an era where machines will be doing things we simply haven’t seen them do before.<\/span><\/p>\n \u00a0<\/strong><\/span>At the moment, much of the inner workings of today’s AI is not transparent due to proprietary algorithms or complexity involved.<\/span><\/p>\n This creates a trust deficit and reluctance to let machines make \u00a0decisions that affect people\u2019s lives.<\/span><\/p>\n If AI is going to live up to its potential, then the smart machines of the near future will have to be more transparent, explainable, and accountable than the ones we’re familiar with now.<\/span><\/p>\n Legislative and regulatory changes are likely to be put in place in future.<\/span><\/p>\n <\/p>\n Interesting<\/u><\/strong><\/span><\/p>\n Intel recently unveiled its Loihi processing chip, packed with more than two billion transistors, which is one application that was able to identify ten different types of hazardous material by smell alone \u2013 more quickly and accurately than trained sniffer dogs.<\/span><\/p>\n <\/p>\n Titbits<\/u><\/strong><\/span><\/p>\n Adversial Model \u2013 AI vs AI<\/u><\/strong><\/span><\/p>\n The most impressive results available today are usually obtained when this is done via an \u201cadversarial\u201d model \u2013 effectively, two AIs are pitted against each other, with one tasked with creating something based on existing data and the other tasked with finding flaws in the new creation. When these flaws are discovered, the creative network (known as the \u201cgenerator\u201d) learns from its mistakes and eventually becomes capable of creating data that its opponent (the \u201cdiscriminator\u201d network) finds increasingly hard to distinguish from the existing data.<\/span><\/p>\n <\/p>\n Value additions are most welcome<\/span><\/em><\/span><\/p>\n <\/p>\n For regular updates, please register here<\/span><\/p>\n\n
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A Quantum Powered AI<\/span><\/strong><\/span><\/h1>\n
Creative AI<\/span><\/strong><\/span><\/h1>\n
Ethical and Accountable AI<\/span><\/strong><\/span><\/h1>\n