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New scheme to help unorganised food processing sector : Gram Samridhi Yojana

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New scheme to help unorganised food processing sector : Gram Samridhi Yojana

India’s food processing ministry is working on a new scheme — Gram Samridhi Yojana — to bolster the unorganised food processing sector concentrated in rural areas, an official said. About 66% of unorganised food processing units are in rural areas and of these, 80% were family run. 

The Rs 3,000 crore scheme funded by the World Bank and the centre will help cottage industry, farmer producers’ organisation and individual food processors to increase capacity, upgrade technology besides skill improvement, entrepreneurship development and strengthening the farm-tomarket supply chain. 

Niti Aayog has given the in-principle approval to the scheme and now the proposal has gone to the Expenditure Finance Committee for clearance, said the official. 
“To ensure doubling of farmers’ income and employment opportunities in rural areas, we are coming with this scheme for food processing enterprises. This will be a game changer, which will encourage cottage and small enterprise to process local produce, package and market it,” said the official. 
The official said the maximum cap of subsidy to be given to a unit will be Rs 10 lakh, apart from interest subsidy, if they avail of loans. “There is a provision for getting subsidy on bank interest by 3% to 5%,” he said. 

Gram Samridhi Yojana also aims to provide common facility centres and business incubators in rural areas. “The incubator will provide infrastructure and services to support the growth of new food businesses. It will provide equipment and programs to help a businessmen or an entrepreneur launch a new product through development, market launch and growth in sales,” he said. 

World Bank will be giving Rs 1,500 crore while Rs 1,000 crore will be borne by the centre while state governments will put in Rs 500 crore, said the official. 
In the initial phase the scheme will be run in Uttar Pradesh, Andhra Pradesh, Maharashtra and Punjab for a five-year period and thereafter replicated in other states. 

Role Of VLSI Industry In India For Digital Revolution

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The whole domain of computing ushered into a new dawn of electronic miniaturization with the advent of semiconductor transistor by Bardeen (1947-48) and then the bipolar transistor by Shockley (1949) in the Bell Laboratory. Since the invention of the first IC (integrated circuit) in the form of a flip-flop by Jack Kilby in 1958, our ability to pack more and more transistors onto a single chip has doubled roughly every 18 months, in accordance with Moore's law. Such exponential development had never been seen in any other field and it still continues to be a major area of research work.

History and Evolution

The development of microelectronics spans a time, which is even lesser than the average life expectancy of a human, and yet it has seen as many as four generations. Early 60s saw the low-density fabrication processes classified under small-scale integration (SSI), in which transistor count was limited to about 10. This rapidly gave way to medium-scale integration in the late 60s when around 100 transistors could be placed on a single chip.

It was the time when the cost of research began to decline and private firms started entering the competition in contrast to the earlier years, where the main burden was borne by the military. Transistor-transistor logic (TTL) offering higher integration densities that outlasted other IC families like ECL became the basis of the first integrated circuit revolution. It was the production of this family that gave impetus to semiconductor giants like Texas Instruments, Fairchild, and National Semiconductors. Early seventies marked the growth of transistor count to about 1000 per chip, called the large-scale integration.

By mid-eighties, the transistor count on a single chip had already exceeded 1000, and hence came the age of very-large-scale integration or VLSI. Though many improvements have been made and the transistor count is still rising, further names of generations like ULSI are generally avoided. It was during this time TTL lost the battle to MOS family owing to the same problems that had pushed vacuum tubes into negligence, power dissipation and the limit it imposed on the number of gates that could be placed on a single die.

VLSI in India

We know that India understands the power of electronics. The Indian electronics industry and India can fully realize the Digital India dream in itself. When the VLSI industry will be established in our country, we can imagine the dream of Digital India or Make in India. Today, in our country there is no single VLSI manufacturing unit in any state or region. So due to this reason, we can say that India cannot make digital ICs, or digital circuits. For digital ICs or digital equipment, we are totally dependent on other countries.

Even now in Indian electronics industry, our country has not been able to do this easily. It cannot easily manufacture those digital devices, like other countries are fabricating the ICs. The VLSI technique or device fabrication and manufacturing in India can bring a digital revolution in India. These steps will be a part of digital electronics. After this evolution in VLSI Industry, electronics or digital devices will become totally inexpensive and we will no longer need to be dependent on other countries for their products and technology. On the contrary, other countries will be able to use our technology and will buy electronics products made by India. 

VLSI technology plays a very important role in digital devices and electronics field. We hope India will be able to achieve this as soon as possible and realize its dream of Make in India and Digital India.

No One Can Ever Solve A Computer Problem That Is Discovered By Mathematicians

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 No One Can Ever Solve A Computer Problem That Is Discovered By Mathematicians

Mathematicians have found an issue they can't fathom. It isn't so much that they're not keen enough; there essentially is no answer.

The issue has to do with machine learning — the sort of computerized reasoning models a few PCs use to "realize" how to complete an explicit assignment.

Whenever Facebook or Google perceives a photograph of you and proposes that you label yourself, it's utilizing machine learning. At the point when a self-driving vehicle explores a bustling crossing point, that is machine learning in real life. Neuroscientists use machine figuring out how to "read" somebody's musings. The thing about machine learning is that it depends on math. What's more, accordingly, mathematicians can ponder it and comprehend it on a hypothetical dimension. They can compose proofs about how machine learning functions that are supreme and apply them for each situation.

For this situation, a group of mathematicians structured a machine-learning issue called "evaluating the greatest" or "EMX."

To see how EMX functions, envision this: You need to put promotions on a site and boost what number of watchers will be focused by these advertisements. You have promotions pitching to sports fans, feline darlings, vehicle devotees and exercise buffs, and so forth. Be that as it may, you don't know ahead of time who will visit the site. How would you pick a choice of advertisements that will augment what number of watchers you target? EMX needs to make sense of the appropriate response with only a little measure of information on who visits the site.

In other machine-learning issues, mathematicians can normally say if the learning issue can be tackled in a given case dependent on the informational index they have. Could the fundamental technique Google uses to perceive your face be connected to anticipating securities exchange patterns? I don't have the foggiest idea, however somebody may.

The inconvenience is, math is kind of broken. It's been broken since 1931, when the scholar Kurt Gödel distributed his renowned deficiency hypotheses. They demonstrated that in any numerical framework, there are sure inquiries that can't be replied. They're not by any stretch of the imagination troublesome — they're mysterious. Mathematicians discovered that their capacity to comprehend the universe was generally constrained. Gödel and another mathematician named Paul Cohen found a model: the continuum speculation.

The continuum speculation goes this way: Mathematicians definitely realize that there are boundless qualities of various sizes. For example, there are endlessly numerous whole (numbers like 1, 2, 3, 4, 5, etc); and there are vastly numerous genuine numbers (which incorporate numbers like 1, 2, 3, etc, however they likewise incorporate numbers like 1.8 and 5,222.7 and pi).
Be that as it may, despite the fact that there are boundlessly numerous whole numbers and vastly numerous genuine numbers, there are plainly more genuine numbers than there are numbers. Which brings up the issue, are there any vast qualities bigger than the arrangement of whole numbers yet littler than the arrangement of genuine numbers? The continuum theory says, indeed, there are.

Gödel and Cohen demonstrated that it's difficult to demonstrate that the continuum theory is correct, yet in addition it's difficult to refute that it's. "Is the continuum speculation genuine?" is an inquiry without an answer.

In a paper distributed Monday, Jan. 7, in the diary Nature Machine Intelligence, the analysts demonstrated that EMX is inseparably connected to the continuum speculation.
Things being what they are, EMX can take care of an issue just if the continuum speculation is valid. In any case, if it's not valid, EMX can't.. That implies that the inquiry, "Can EMX figure out how to fathom this problem?"has an answer as mysterious as the continuum speculation itself.

Fortunately the answer for the continuum theory isn't vital to the greater part of arithmetic. Furthermore, correspondingly, this changeless puzzle probably won't make a noteworthy deterrent to machine learning.
"Since EMX is another model in machine learning, we don't yet know its helpfulness for growing genuine calculations," Lev Reyzin, an educator of arithmetic at the University of Illinois in Chicago, who did not take a shot at the paper, wrote in a going with Nature News and Views article. "So these outcomes probably won't end up having useful significance," Reyzin composed.

Running up against an unsolvable issue, Reyzin composed, is a kind of plume in the top of machine-learning specialists.

It's proof that machine learning has "developed as a numerical order," Reyzin composed.
Machine adapting "now joins the numerous subfields of science that bargain with the weight of unprovability and the unease that accompanies it," Reyzin composed. Maybe results, for example, this one will convey to the field of machine taking in a sound portion of quietude, even as machine-learning calculations keep on altering our general surroundings. " 
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