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Artificial intelligence (AI) has promoted every aspect of our lives – the way we talk about how we work, buy, play and do business – AI tools are everywhere where we see.
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Already, specific business benefits are providing in almost any industry you can name – but it’s clear that we have just started. The available technology will undoubtedly look like a calculator for a decade today. Computers will be more smart, fast and fast to perform only the tasks that can traditionally perform by people, such as making complex decisions or being in creative thinking. Here is a review of some options that looks like science fiction today, but can be part of the reality of everyday before you think!
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Today, most AI applications have been classified as “tight” or “weak” AI, which means that in some ways they meet the general standards that we have for intelligence – the most prominent learning ability – they usually perform only a specific task for which they are designed. Really intelligent (let’s say, “naturally intelligent” devices are not “designs” for any work, but are ready to perform a number of tasks needed to perform them. The search for “General AI” is dealing with the development of smart machines that can be purchased in the same way.
It helps to think about how to improve the AI applications available to us today. For example, Amazon uses Alexa to understand what we are saying. However, this is less or less its “smartness” limit – once it understands our instructions, it performs them fully programmically.
When leading to more general requests of AI, home assistant devices will have the ability to “think” more actively. In addition to improving the natural language processing technology (NLP) and improving fluid conversations, it would be better to predict what we need or how we will work and interfere with how to adjust it. This means that everything can be meant to take care of our car if needed, from the order to monitor our health, and if the police find out the search in our homes, then calling the police to everything can mean. The important thing is that it will do all this because it is calculated that it is the best thing in a particular situation, rather than that it was clearly asked to do so.
Computing Power is the engine of AI, and the great jumps we have seen in the last ten years are largely lower in the growing amount of treatment with us. In particular, the use of graphics treatment units (GPUs) has directly caused many deep techniques and applications to learn from the beginning of the past decade, which are very useful today.
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The next level is likely to unlock the quantum calculations, such as biological and neuromorphic computing, with other treatment functions.
Quantum computing is certainly not an easy concept to explain in the cutting size part of such text, but it mainly works using strange and somewhat confused (if you do not have PhD in physics!) The ability to have subtomatic particles in multiple states at the same time. You may find more detailed conversations here, but it is enough to say that it is ideologically capable of performing some calculations than 100 trillion times faster than today’s fastest computers.
To develop permanently to become smart, the machine learning models will inevitably be more. One of today’s most sophisticated “Generato” GPT-3 of AI-Models-Openai already has more than 175 billion different parameters in its code. This requires an increasing amount of treatment. In addition, the maximum treatment means that we will be able to produce large quantities of “artificial” data for training purposes, and reduce the need to collect real data for many applications to feed the algorithm.
As a good example, think about the data needed to train your self -powered car. The Algorithm needs to show many hours of driving experience to learn how to securely visit the streets. More strength of treatment means that more accurate and realistic imitation can be made so that the maximum learning can be performed in an artificial environment. Not only is it possible that it is cheap and safe, but it can also be performed with a very fast speeding thousand, which can be compressed for real -time driving lessons for a short period of computer driving.
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Although a truly useful quantum calculation can be a way out of applications out of special academic research, other technologies such as neuromorphic computing will produce waves in the meantime. Their purpose is to imitate the human brain’s “flexible” abilities to adapt to the treatment of new forms of information. Intel recently revealed its Louis Treatment Chip, filled with more than two billion transactions, which is an application that is capable of identifying only ten different types of dangerous materials in the odor – faster and accurately trained by trained Sniffer dogs.
These days we can see art, music, poetry and even computer codes made by AI. Most of this has been made possible by the ongoing development of the “Generative” AI (which includes the aforementioned GPT -3 model). This is an expression used to explain AI when its function is to create new data rather than merely analyzing and understanding the current data.
With Generative AI, analysis and understanding is still the first step in the process. He then takes what he has learned and uses it to build additional examples of the models he has studied. The most impressive results available today are usually achieved when it is done by a “contradictory” model – effectively two AIs are deposited against each other, one is entrusted with the task of making something based on current data and the other, which is assigned to find a reduction in new creation. When these flaws are discovered, the creative network (known as the “generator”) learns from its mistakes and eventually succeeds in making data that its opponent (discrimination “network) is difficult to distinguish with the current data.
Although it is very good in itself that a computer can create, the cost of the amount is in the ability to make artificial data that can be used to train other machines. For example, facial recognition algorithms depend on people to learn to recognize people, accessing a huge library with pictures of people’s faces, as self -run cars need a lot of driving experience to learn driving. This ability to make artificial data will lead us to an era where AI’s over -riding applications are not just those “wow” factor machines that we have never seen before. Instead, the focus will turn into a really valuable and useful use of AI, which is working to solve major challenges in the real world.
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It clearly leads to the final, but somehow the most important way in which AI will be ready. Currently, most of the internal work of today’s AI is hidden. Sometimes it is locked in a proprietary algorithm, which is considered to be a firmly safe business secret, while sometimes it is very complicated to understand most of us.
In any way, this creates an important issue – we are making important decisions that can affect people’s lives in the hands of machines that we do not fully understand. It has great consequences for confidence – and if people do not trust the AI, the idea of making decisions is unlikely to be relieved even when the data clearly shows that it is likely to do the right thing.
If the AI wants to be in accordance with its abilities, in the near future, smart machines should be more transparent, explainable and responsible than they are aware of. We see that steps are being taken to ensure that the formation of organizations like AI, Openi and the British Government’s Allen Touring Institute is a matter of forming organizations. Of course, some people will see unlimited opportunities that offer AI to change profits, and lure guidelines and recommendations made by such groups to revolve or just ignore or ignore. Therefore, we are also likely to see legislation and legislative changes that are introduced to minimize the ability to harm the loss. All these solutions that work together can help ensure that AI lives according to its abilities.
For more information about the topic of artificial intelligence, see my book ‘The Intelligence Revolution: Changing your business with AI’.
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Bernard Marm is an impressive and thinking manager in a world -renowned future, business and technology. He is the writer of 18
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