In this era of entrepreneurs, the owners or managers of business enterprises who, by risk and initiative, attempt to make profits, where people are capable of reaching the heights of the sky but many aren’t able to because of lack of resources and some place to start their work.

Keeping this problem in mind, a whole new start-up ecosystem for the young entrepreneurs is provided by these centres called incubation centres.

Incubation centres are spaces provided by companies or universities for start-ups, for new root ideas to be catered. Just like eggs are incubated by hens in order to produce healthy chickens, the start-ups are incubated in these business incubation centres where they get support to develop their business before hatching into the market.

The National Business Incubation Association (NBIA) defines business incubators as a catalyst tool for either regional or national economic development.

NBIA categorizes their members’ incubators by the following five incubator types:

• academic institutions;
• non-profit development corporations;
• for-profit property development ventures;
• venture capital firms, and
• combination of the above.

Incubation centres operated by universities cater for ideas that take root while students are still studying to bridge the gap between rough concepts and fully-fledged businesses able to step into a commercial environment. The advantages of these companies are that there are a ready-made network of advisors and peers to help them if and when needed.

Entrepreneurs who wish to enter a business incubation program need to apply for admission. Acceptance criteria vary from program to program, but in general, only those with feasible business ideas and a workable business plan are admitted. It is this factor that makes it difficult to compare the success rates of incubated companies against general business survival statistics.

Now the question arises, “Who pays rents for these spaces and the resources provided?”

About one-third of business incubation programs are sponsored by economic development organizations. Government entities (such as cities or counties) account for 21% of program sponsors. Another 20% are sponsored by academic institutions, including two- and four-year colleges, universities, and technical colleges.

In many countries, incubation programs are funded by regional or national governments as part of an overall economic development strategy. In the United States, however, most incubation programs are independent, community-based and resourced projects. The U.S. Economic Development Administration is a frequent source of funds for developing incubation programs, but once a program is open and operational it typically receives no federal funding; few states offer centralized incubator funding.

Rents and/or client fees account for 59% of incubator revenues, followed by service contracts or grants (18%) and cash operating subsidies (15%).

The incubation centres help the entrepreneurs with guidance and mentorship, networking, and costs of operation.

The only drawback possible is that working in an incubation centre could be noisy and distracting due to the other people around.

So, if any of you wants to do be an entrepreneur, has some perfect B-Plan and are ready for the start-up but don’t know where to start and how, just go for these incubation centres, work hard, and come out with flying colors. Make your dreams come true.

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What is AI and what is not AI?

Not an easy question!!!!

We can see almost anything from statistics and business analytics to manually encoded if-then rules called AI.

Why is this so?

Why is the public perception of AI so nebulous?

We will look into this article.

1. Reason 1 – No officially agreed definition

Even AI researchers have no exact definition of AI. There is an old (geeky) joke that AI is defined as “cool things that computer can’t do.”

Irony: AI can never make any progress – as soon as we find a way to do something cool with a computer, it stops being an AI problem.

Fifty years ago, for instance, an automatic method for search and planning were considered to belong to the domain of AI. Nowadays such methods are taught to every computer science student.

Similarly, certain methods for processing uncertain information are becoming so well understood that they are likely to be moved from AI to statistics or probability very soon.

2. Reason 2 – the legacy of science fiction

The confusion about the meaning of AI is made worse by the visions of AI present in various literary and cinematic works of science fiction. Sci-fi stories often feature friendly humanoid servants but can sometimes start to wonder if they can become human.

Another class of humanoid beings in sci-fi espouse sinister motives and turn against masters.

Often the robothood of such creatures is only a thin veneer on top of a very humanlike agent as sci-fi needs to be relatable by human readers.

3. Reason 3 – What seems easy is actually hard!!!

Another source of difficulty in understanding AI is that it is hard to know which tasks easy and which ones are hard. E.g.  Look around and pick up an object in your hand, then think about what you did:

• Used your eyes to scan surroundings
• Figures out where are some suitable objects for picking up.
• Chose one of them and plan a trajectory for your hand to reach that one, then moved your hand by contracting various muscles in sequence.
• Managed to squeeze the object with just the right amount of force to keep it between your fingers.

It can be hard to appreciate how complicated all this is but sometimes it becomes visible when something goes wrong:

The object you pick up is much heavier or lighter than you expected.

Someone else opens a door as you are reaching for the handle and then you can find yourself seriously out of balance.

Usually, this kind of tasks feel effortless but that feeling belies million of years of evolution and several years of childhood practice.

While easy for us grasping objects but for a robot, it is extremely hard and it is an area of active study.

…And What seems hard is actually easy

By contrast, the task of playing chess and solving mathematical can seem to be very difficult but it has turned out that such tasks are well suited to computers, which can follow simple rules and compute many alternative move sequences at a rate of billions of computations per second.

Similarly, while in depth mastery of mathematics requires (what seems like) human intuition and ingenuity, many (but not all) exercises of a typical high level or college course can be solved by applying a calculator and simple set of rules.

So, the more useful definition for AI could be

An attempt at a more useful definition would be to list properties that are characteristics to AI, in this case, is autonomy and adaptivity.

Key Terminology

Autonomy – The ability to perform tasks in complex environments without constant guidance by the user.

Adaptivity – the ability to improve performance by learning from experiences.

Words can be misleading

While defining and talking about AI we have to be cautious as many of the words that we use can be misleading. Common examples are learning, understanding and intelligence.

For e.g. we may say that a system is intelligent perhaps because it delivers accurate navigation instructions or detects the sign of melanoma in photographs of skin lesions but actually the word “intelligent” suggests that the system is capable of performing any tasks that an intelligent person would be able to perform such as going to grocery store and cooking dinner, washing and folding laundry and so on.

Likewise, when we say that a computer vision system understands images because it is able to segment an image into distinct objects such as other cars, pedestrians, buildings, the road and so on. The word “understand” easily suggests that the system also understand that even if a person is wearing a t-shirt that has printed a photo of a road printed on it and it is not okay to drive on that road (and over the person).

In both of the above cases, we’d be wrong.

Note:

Watch out for ‘suitcase word’

Marvin Minsky, a cognitive scientist and one of the greatest pioneers in AI, coined the term “suitcase word” for the terms that carry a whole bunch of different meanings that came along even if we intend only one of them. Using such terms increase the risk of misinterpretations such as shown in the examples above.

Intelligence is not a single dimension like temperature.

We can compare different temperatures and tell which one is higher and which one is lower.

We even have a tendency to think that it is possible to rank people with respect to their intelligence using IQ.

However, in the context of AI, it is obvious that different AI systems cannot be compared on a single axis of dimension in terms of their intelligence.

Is a chess playing algorithm more intelligent than a spam filter, or is a music recommendation system is more intelligent than a self-driving car? These questions make no sense.

This is because AI is narrow, being able to solve one problem tells us nothing about the ability to solve another different problem.

Why you can say “a pinch of AI” but not “an AI”?

The classification into AI vs non-AI is not a clear yes-no dichotomy. While some methods are clearly AI and other are clearly not AI, there are methods that involve a pinch of AI, like a pinch of salt. Thus it would sometimes be more appropriate to talk about the “AI ness (as in happiness or awesomeness) rather than something is AI or not.

Note: AI is not a countable noun.

When discussing AI, we would like to discourage the use of AI as a countable noun: one AI, two AI and so on. AI is a scientific discipline like mathematics and biology. This means that AI is a collection of concepts, problems and methods for solving them.

Because AI is a discipline, you should not say “an AI” just like we don’t say “a biology”. This point should also be quite clear when you try saying something like “we need more artificial intelligences” that just sounds wrong, doesn’t it? (It does to us.)

The use of AI as a countable noun is of course not a big deal if what is being said otherwise makes sense but if you’d like to talk like a pro, avoid saying “an AI” and instead say “an AI method”.

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