The Truth About ARTIFICIAL INTELLIGENCE In 3 Minutes | By Tech Prince |

                                       

Artificial Intelligence is mainly divided into three categories:

1. Narrow AI: It is the one which we see in our mobiles like Siri, Alexa, Cortana etc. which is capable        of doing a single task only.

2. Strong AI: It is an AI which has the Intelligence of a human.

3. Super AI: It is an AI which is Smarter than a human.

                     

When the average person thinks about ‘AI’, or artificial intelligence, certain themes or topics often come to mind. Sophia the robot. Alex Garland’s 2014 box office hit Ex-Machina. Robots taking our jobs and the fear that they will one day take over the world. This view is not only narrow-minded but also fails to acknowledge all the good AI can and is already doing for people all over the world — especially in the food space. The truth is, AI, when used for good, isn’t scary and it’s not at all what you think is. And most importantly, AI has applications that far exceed the confines of real, literary, and cinematic robots.

Artificial Intelligence has been mistaken by everyone

We are in the era of Artificial Intelligence and everyone's hyped about AI. And many of them are believing in myths like will it become Skynet, will AI destroy us etc. I am not saying it will not happen but noone can say for sure it will happen.

My point is everyone is judging a machine or a thing which hasn't been invented or which has been invented imperfect. Let's take a example if you go 10 years before where you have keypad mobiles and when you go a bit further like 2012 and 2013, where you have Smartphones. How did you get to know that it is fit for the society. How?

Unless you use it, you don't know how it will work, what are the features etc. then how can you say that a machine which has been invented properly will destroy the whole world. how can you say for sure that this will happen? Yeah who believe's it. Everyone believe's in magic but not in logic!!

Everyone thinks that AI will takeover some jobs in the future

Yes, AI will takeover some of the jobs, at least this we can say for sure. AI will be replacing jobs like:

Drivers: We have seen Driverless cars and many companies are turning their automobiles into driverless cars. Some of them are:  Mercedes-Benz, General Motors, Continental Automotive Systems, Autoliv Inc., Bosch, Nissan, Toyota, Audi, Volvo etc.

Chefs and Janitors : Yes, chefs and janitors are going to get replaced in the near future.

Jobs which require no skill and experience required are going to get replaced.

The Curse of scattered ML algorithms

Some of the main risks that scattered ML algorithms bring are:

• Algorithms might be trained using a limited set of features and data resulting in the wrong or sometimes dangerous business decisions either inside or outside the business area.
• While optimizing local operational decisions, such algorithms might unintentionally negatively affect other business areas or even global operations.
• Such individual algorithms can be easily manipulated and mislead in making wrong decisions by internal or external actors adding a major new category of cybersecurity risks.
• Training some machine learning algorithms might require expensive computing power adding high costs to small business units. In many cases this caused business units to abandon AI completely based on the false impression of high costs of adoption.

AI technologies are not yet ready for industrial adoption. Is it a myth?

Like humans, AI algorithms need more real-world experience that might include more data created through algorithms’ own trials and errors in the real world.

Therefore, it would be unfair and technically wrong to judge AI solutions in the early stages while they still have no or little experience. This is one of the most common mistakes done today and usually lead to frustration and misunderstanding around the maturity of AI underlying models. We’ve to give AI-powered solutions time to learn and be carefully evaluated before deploying them in the enterprise.

For instance, machine learning capabilities which gained enough real-world experience such as computer vision (CV) and Natural Language Processing (NLP) are the most mature and widely adopted parts of AI today. They’re the cognitive engines behind many industrial and consumer applications and products with the most positive impact on business and our personal life so far.

Additionally, one of the key capabilities AI systems must have per design is that it should have the ability to continuously learn as well as dynamically leverage effective learning approach(es) over time. Selecting the right initial architecture as well as continuous learning approaches such as supervised, unsupervised, reinforcement learning or a mix of them is very important in a successful AI adoption. Lifelong Continues Learning (LLCL) is one of the main and most promising AI research areas today. However, it continues to be a challenge for the current machine learning and neural network models since the continual acquisition of new information from non-stationary data sources generally lead to catastrophic forgetting of previously learned knowledge or abrupt decrease in the precision.

These are some of the misunderstandings about AI.

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