Tag Archives: #technology


Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.


The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names. This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.


  • AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
  • AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
  • AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
  • AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
  • AI achieves incredible accuracy through deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
  • AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.


Artificial intelligence is going to change every industry, but we have to understand its limits. The principle limitation of AI is that it learns from the data. There is no other way in which knowledge can be incorporated. That means any inaccuracies in the data will be reflected in the results. And any additional layers of prediction or analysis have to be added separately. Today’s AI systems are trained to do a clearly defined task. The system that plays poker cannot play solitaire or chess. The system that detects fraud cannot drive a car or give you legal advice. In fact, an AI system that detects health care fraud cannot accurately detect tax fraud or warranty claims fraud. In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like humans.

Why angel investors prefer Tech start-ups over Non-Tech?

Start-ups (which indirectly fall under MSEs category of taxation) since 2014 have collected around $100 billion and are on the ever-accelerating way to mark its way to $500 billion by 2025, with a projection to create over 35 – 40 lakh jobs. 

It was a beautiful day for Mr. Singh. He had invested in an idea introduced by a bunch of boys who had recently graduated out of an Engineering College. It was something related to irrigation technology with the name “Ivy-Irri Tech”. Mr. Singh had no idea what it was, but his financial advisor and accountant advised him that the investment would garner good profit in a very short period of time. After he found everything to be appropriate, he wrote off a check for Rs 3 crore for 3,000 shares to Ivy-Irri Tech boys. Today, he received the triple of his investment (i.e., Rs 9 crore) as the start-up was brought under the banner of a multinational corporation (MNC).

Mr. Singh was indeed an ‘angel’ who invested in the start-up seeing the growth projection as calculated by the discounted cash flow (DCF) method. He knew and took all the risks on the idea. Like Mr. Singh, there are a number of high-value individuals in our nation who are approached to invest in a small idea, which the ones presenting are able to convince (or show) to be of big worth in a short period of time.

A few days went by and the boys again contacted him over the notice they received from the Income Tax Dept. The notice stated that they had to pay 30% as ‘Angel Tax’ clause of Section 56(2)(vii b) of the Income Tax Act, 1961.

These start-ups operate in a very vulnerable environment and anything can happen any moment. All the money made in the first half of the day may just vanish off by second. The basic principle of start-ups is a low investment to high yield, in less time.

According to Economic Times, “Angel tax is a term used to refer to the income tax payable on capital raised by unlisted companies via the issue of shares where the share price is seen in excess of the fair market value of the shares sold. The excess realisation is treated as income and taxed accordingly.” This is charged when the initial “angel” investor is an Indian, while foreigners are exempted from it as that’d just add more to Foreign Direct Investments (FDI) category. Also, the value of start-ups is counted against the industry suggested method of DCF with the net value present (NVP) method that increases the difference between the projected margins to the excess premium earned.

Hence, nowthe start-up will have to pay the excess of what they received of initial capital (i.e., Rs 3 crore).  In shares & dividend terms – Mr. Singh bought 3000 @ Rs 10000 each. He sold them (the startup sold it to the MNC) at a premium (excess from Market Value – profit) of Rs 30,000 for each share. Hence, for 3000 shares the excess profit is Rs 6 crore. Now 30% of Rs 6 crore is Rs 1.80 crore and that is what the start-up is charged as “Angel Tax”.

This is a major de-motivation to the hardworking, innovative minds that have worked hard to put up the efforts to bring their dream into happening, just like the “Ivy-Irri Tech” chaps and returned the initial investment in a triple in less than some years, but now are a victim of the ‘Angel Tax’.

However, the income tax regimes in our nation, which are duly unregulated at the helm of dysfunctional bureaucracy and call for immediate reforms at a great extent, do not spare even the ‘angels’. This taxation regime has led to the inclination of angel investors into investing in tech start-ups and deviating from the non-tech cohorts. The falling of start-ups into MSEs category, the very narrow definition of start-ups, and the bureaucracy which looks for an opportunity to put to their advantage, are the reasons for non-tech start-ups being not worth investment against hassles.

Of the limited few exemptions in Angel Tax, the angel investors tend to avoid the non-tech sector as there’s a very obstructive measure which restricts the investment into immovable objects. So if the start-up in non-tech sectors, would involve investment in immovable assets (which is the case in most non-tech start-ups) then the investment would not fall into exemption into start-up’s seed funding and thereby incurring additional taxation. The ruling Govt. has presented a very ambitious plan to lead India to a $5 trillion economy for which there needs to be a safe growth rate in the economy at 11.3% (also assuming rupee falls to the dollar, further) for the next five years with no exception contrary to the present which is less than 4%. Further, with Moody’s downgrading India to ‘Baa3’ category, just one rank above “junk” category, the onset of FDIs flowing into Indian start-ups seems reclusive and does not seem to recover anytime soon. So, the Income Tax Act, 1961 needs to reform from its very core to match up the economic challenges of the 21st century for Indian investors to keep the market afloat and its operations flared up. Time is money, and neither of that we do have.