Roughly 44 percent of companies are looking to make serious investments in AI and integrate it into their businesses. And of the 9,130 patents received by IBM inventors in 2021, 2,300 were AI-related.

It seems likely that AI is going to (continue to) change the world. But how, exactly?

The Evolution of AI

AI’s influence on technology is due in part because of how it impacts computing. Through AI, computers have the ability to harness massive amounts of data and use their learned intelligence to make optimal decisions and discoveries in fractions of the time that it would take humans.

AI has come a long way since 1951, when the first documented success of an AI computer program was written by Christopher Strachey, whose checkers program completed a whole game on the Ferranti Mark I computer at the University of Manchester.

Since then, AI has been used to help sequence RNA for vaccines and model human speech, technologies that rely on model- and algorithm-based machine learning and increasingly focus on perception, reasoning and generalization. With innovations like these, AI has re-taken center stage like never before — and it won’t cede the spotlight anytime soon.

The Impact of AI on Society–

HOW AI WILL CHANGE WORK

One of the most promising new technologies is neuromorphic processing. Neuromorphic means “like the brain.” Dedicated circuits are used to mimic the way dynamic cells in the brain operate. They do not run any programs but are capable of learning, and just like actual brain cells, they all work simultaneously rather than sequentially. Neuromorphic cortical models of artificial intelligence are based on the structure and function of the neocortex, the brain’s outer region responsible for complex cognitive processes and are small, faster and less power-hungry than computers.

Research into these and other brain structures is expected to lead to greater levels of intelligence and better cognitive performance than in previous types of artificial intelligence. With several million nodes, these artificial cortical networks are still far from simulating human intelligence.

Like the composition of the brain, which contains many different structures, it may be necessary to use different types of neural networks to perform specific functions. The neocortex is just one part of the brain responsible for cognition and intelligence. It has massive connections to the thalamus, the hippocampus and the cerebellum, all examples of brain regions important for different cognitive aspects.

Modeling these regions and the neocortex could lead to more advanced AI systems. The thalamus is the central hub that receives sensory information in the brain. Modeling the thalamus could improve the ability of AI to process sensory information, such as auditory, tactile and visual data.

The hippocampus is involved in spatial navigation and the creation of long-term memories. Modeling the hippocampus’s functions could improve AI systems’ ability to learn and selectively form long-term memory.

The cerebellum has massive connections to all regions of the neocortex. Modeling the cerebellum could lead to the simultaneous processing of incoming data while the AI is learning something new, like driving a car.

While the neuroscience understanding of these brain regions is still incomplete, sufficient information is available to build models that may answer open questions and fill in some of the blanks through experimentation. One day, cortical neuromorphic neural networks may displace the neural networks driving artificial intelligence today and have been responsible for its many successes.

One key difference is their training method. Current neural networks require millions of examples and an error feedback algorithm to adjust the parameters. These training sessions can take weeks using expensive, powerful computers costing millions.

Cortical neuromorphic neural networks learn from few examples and are thus cheaper to deploy. Neuromorphic processing eliminates the need for colossal computer resources. Continuous learning adds experience, creating more accurate outcomes.

Cortical neural networks are expected to be used in products that range from speech recognition to image processing, space exploration, healthcare, and robotics within the next five years. The development of cortical neural networks may lead to the emergence of artificial general intelligence (AGI), the holy grail of artificial intelligence. Humanity will benefit from the emergence of AGI in turbocharging the global economy in providing a multiplier for human ingenuity and safety. AGI is likely to benefit humankind much like previous economic revolutions. The internet and computers changed the way we do business. There will be a similar shift in how humans occupy their time in the future.

AI is a fast-moving field, and companies cannot afford to stand still. To enable personnel, it is imperative to take action now to enable employees to upgrade their skills to meet the challenges of the future. Seek out opportunities to train your employees on this new technology in order to fully embrace it across your organization.

Artificial Intelligence (AI) at Present

Before going deep dive into AI in future, first, let’s understand what is Artificial Intelligence and at what stage it is at present. We can define AI as, “It is the ability of machines or computer-controlled robot to perform task that are associated with intelligence.” So, AI is computer science, which aims to develop intelligent machines that can mimic human behaviour.

Based on capabilities, AI can be divided into three types that are:

• Narrow AI: It is capable of completing dedicated tasks with intelligence. The current stage of AI is narrow AI.
• General AI: Artificial General Intelligence or AGI defines the machines that can show human intelligence.
• Super AI: Super AI refers to self-aware AI with cognitive abilities that surpass that of humans. It is a level where machines can do any task that a human can do with cognitive properties.

At the current stage, AI is known as Narrow AI or Weak AI, which can only perform dedicated tasks. For example, self-driving cars, speech recognition, etc.

Myths about Advanced Artificial Intelligence

1. Superintelligence by the year 2100 is not possible.

The reality about the possibility of superintelligence is that currently, we can’t determine it. It may occur in decades, or centuries, or may never, but nothing is confirmed. There have been several surveys in which AI researchers have been asked how many years from now they think we will have human-scale AI with at least a 50% chance. All of these surveys have the same conclusion: The world’s leading experts disagree, so we don’t know. For example, in such a survey of AI researchers at the 2015 Puerto Rico AI conference, the (average) answer was by 2045, but some researchers estimated hundreds of years or more.

2. I will replace all human jobs.

It’s certainly true that the advent of AI and automation has the potential to disrupt labour seriously – and in many situations, it is already doing just that. However, seeing this as a straightforward transfer of labour from humans to machines is a vast oversimplification.

With the development of AI, a revolution has come in industries of every sector, and people fear losing jobs with the increased development of AI. But in reality, AI has come up with more jobs and opportunities for people in every sector. Every machine needs a human being to operate it. However, AI has taken over some roles, but it reverts to producing more jobs for people.

3. Super-intelligent computers will become better than humans at doing anything we can do

As discussed above, AI can be divided into three types, Weak AI, which can perform specific tasks, such as weather Prediction. General AI; Capable of performing the task as a human can do, Super AI; AI capable of performing any task better than human.

At present, we are using weak AI that performs a particular task and improves its performance. On the other hand, general AI and Super AI are not yet developed, and researches are going on. They will be capable of doing different tasks similar to human intelligence. However, the development of such AI is far away, and it will take years or centuries to create such AI applications. Moreover, the efficiency of such AI, whether it will be better than humans, is not predictable at the current stage.

4. AI does not require human intervention.

People also have a misconception that AI does not need any human intervention. But the fact is that AI is not yet developed to take their own decisions. A machine learning engineer/specialist is required to pre-process the data, prepare the models, prepare a training dataset, identify the bias and variance and eliminate them, etc. Each AI model is still dependent on humans. However, once the model is prepared, it improves its performance on its own from the experiences.

How can Artificial Intelligence be risky?

Most of the researchers agree that super AI cannot show human emotions such as Love, hate or kindness. Moreover, we should not expect an AI to become intentionally generous or spiteful. Further, if we talk about AI to be risky, there can be mainly two scenarios, which are:

1. AI is programmed to do something destructive:

Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war resulting in mass casualties. To avoid being dissatisfied with the enemy, these weapons would be designed to be extremely difficult to “turn off,” so humans could plausibly lose control of such a situation. This risk is present even with narrow AI but grows as levels of AI intelligence and autonomy increase.

2. Misalignment between our goals and machines:

The second possibility of AI as a risky technology is that if intelligent AI is designed to do something beneficial, it develops destructive results. For example, Suppose we ask the self-driving car to “take us at our destination as fast as possible.” The machine will immediately follow our instructions. It may be dangerous for human lives until we specify that traffic rules should also be followed and we value human life. It may break traffic rules or meet with an accident, which was not really what we wanted, but it did what we have asked to it. So, super-intelligent machines can be destructive if they ask to accomplish a goal that doesn’t meet our requirements.

Future impact of AI in different sectors

Healthcare:

AI will play a vital role in the healthcare sector for diagnosing diseases quickly and more accurately. New drug discovery will be faster and cost-effective with the help of AI. It will also enhance the patient engagement in their care and also make ease appointment scheduling, bill paying, with fewer errors. However, apart from these beneficial uses, one great challenge of AI in healthcare is to ensure its adoption in daily clinical practices.

————————FAQs———————

1.What is artificial intelligence?

• It’s the study of methods for making computers behave intelligently. Roughly speaking, a computer is intelligent to the extent that it does the right thing rather than the wrong thing. The right thing is whatever action is most likely to achieve the goal, or, in more technical terms, the action that maximizes expected utility. AI includes tasks such as learning, reasoning, planning, perception, language understanding, and robotics.