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Bots take over jobs of 12,000 employees

Bots take over jobs of 12,000 employees at Wipro

Co says it has achieved productivity worth 12,000 people over 140 customer engagements in 1,800 HOLMES bots in IT services.

Bots take over jobs of 12,000 employees at Wipro
Malini BhuptaMoneycontrol News

Bots are actually replacing humans at Wipro. India’s third largest IT services company, which is in the process of downsizing 10 percent of its workforce this year, kicked off Project NextGen in 2015 to improve productivity and automate processes. While the company denied plans to downsize, it said it achieved productivity worth 12,000 people over 140 customer engagements in 1,800 HOLMES bots in IT services.

Automation is, in fact, a reality now and progressively more jobs could become redundant as the pace of deployment increases.

In computer programming lingo, a bot, which is short for robot, is a program that operates as an agent for a user or another program or simulates a human activity. Wipro’s HOLMES bot uses a natural language processing (NLP)-based chat interface.

Wipro kicked off its transformative programme ‘Project NextGen’ in 2015, in consultation with McKinsey, to improve productivity of teams and automate processes. Senior managers claim teams were asked to execute the same projects with 10-15 percent less manpower, as McKinsey outlined several levers to improve productivity. Insiders claim that this was a dry run for this year’s downsizing, as teams were asked to function without 10-15 percent of the existing manpower.

The move to make do with fewer people was part of suggestions made by McKinsey to enhance productivity and help automate processes.

A very senior manager at Wipro says: “Project NextGen began very well and a few hundred senior managers were appointed as change leaders who would implement McKinsey’s recommendations. But what we did not realize was that it was a simulation of sorts to downsize later.”

From slashing coffee breaks and taking away mobile phones of employees during “Silent Hours” to real-time monitoring progress of tickets (problems raised by clients) on “White Boards” to “Huddle Meetings,” McKinsey came up with an exhaustive list of levers Wipro’s managers could use to enhance productivity of teams and cut down on waste.

But the net outcome of these initiatives was to reduce the strength of every team by 10-15 percent. Automation was supposed to be a big piece of this initiative and managers were assured that there would be no retrenchment at the end of the exercise.

In response to a questionnaire sent by Moneycontrol on gains from Project NextGen automation initiatives and consequent impact on jobs, Wipro said: “In FY17, Wipro generated productivity worth over 12,000 persons across 140+ customer engagements in over 1,800 cumulative instances of HOLMES bots in IT services, in the areas of application development, application support & maintenance and infrastructure services. Employees who are involved in such projects are then retrained and upskilled to be redeployed to handle higher value tasks.”

However, some of the change leaders that Moneycontrol spoke to said that the level of automation achieved was no more than 2-3 percent of the target, as India either lacked the requisite talent pool or that customers were not always ready for it. In many cases, privacy laws of other countries also came into play for automation.

Wipro is not looking just at automation to cut costs. It is also lowering the “Bulge Mix” –average work experience of a team. Managers have been told to lower the Bulge Mix of teams to less than 4 years. So, if a team has 40 people and the average work experience of individuals is between 0-3 years and the manager has little less than ten years, then the bulge mix will come down to less than 4 years.

Wipro, however, said: “There is no plan to change the bulge mix. We will continue to hire skilled resources at the middle and senior levels from the market.”

This also indicates that Wipro is planning to weed out more senior and experienced people who are managing the junior employees with less than 3-years experience. This is a big lever to cut costs but it also puts at risk managers with more than 10-years experience.

The existing IT services companies have so far leveraged upon the wage arbitrage that existed between the developed world and India. But the sector did little to prepare for a wave of disruptive technologies like cloud computing, which is making data centres and the support functions around it redundant.

With clients pressurizing companies to automate repetitive processes, Wipro roped in McKinsey to help it migrate from the existing model to stage where it could morph into a digital services company.

Blockchain: The Missing Link Between Genomics and Privacy?

Blockchain

 
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Positive reinforcement

2016 became huge for improvements in synthetic intelligence and machine gaining knowledge.

But 2017 may additionally properly supply even more. Here are 5 key matters to stay up for.

Positive reinforcement

AlphaGo’s historical victory towards one of the excellent Go players of all time, Lee Sedol, became a landmark for the sphere of AI, and particularly for the approach called deep reinforcement mastering.

Reinforcement learning takes an idea from the methods that animals learn how positive behaviors have a tendency to bring about advantageous or poor final results. Using this approach, a laptop can say, determine out the way to navigate a maze with the aid of trial and errors and then accomplice the tremendous final results—exiting the maze—with the moves that led as much as it. This we could a machine study without practice or maybe express examples. The concept has been around for many years, but combining it with big (or deep) neural networks offers the strength had to make it paintings on truly complicated troubles (like the game of Go). Through relentless experimentation, as well as evaluation of previous games, AlphaGo found out for itself how to play the sport at a professional degree.

The hope is that reinforcement learning will now show beneficial in many actual-international conditions. And the latest launch of numerous simulated environments must spur progress at the necessary algorithms by means of growing the variety of abilities computer systems can accumulate this manner.

Dueling neural networks

At the banner AI academic accumulating held in Barcelona, the Neural Information Processing Systems conference, plenty of the excitement a new system getting to know technique called generative hostile networks.

A.I. will replace half of all jobs in the next decade

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5 Things To Watch In AI And Machine Learning

Five Things To Watch In AI And Machine Learning In 2017

 

Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI). During the year, we saw nearly every high tech CEO claim the mantel of becoming an “AI Company”. However, only a few companies were actually able to monetize their significant investments in AI, notably Amazon AMZN +0.71%, Baidu , Facebook FB +1.75%, Google GOOGL +3.77%, IBM IBM -0.02%, Microsoft MSFT +0.29%, Tesla Motors TSLA +1.75% and NVIDIA NVDA -1.29%. But 2016 was nonetheless a year of many firsts. As a posterchild for the potential for ML, Google Deep Mind mastered the subtle and infinitely complex game of GO, soundly beating the reigning world champion. And more than a few cool products were introduced that incorporated Machine Learning, from the first autonomous vehicles to new “intelligent” household assistants such as Google Home and Amazon Echo. But will 2017 finally usher in the long-promised age of Artificial Intelligence?

NVIDIA's Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

NVIDIA’s Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

Two domains: AI and Machine Learning. These terms are not interchangeable. Machine learning, a completely different way to program a computer by training it with a massive ocean of sample data, is real and is here to stay. General Artificial Intelligence remains a distant goal and is perhaps 5-20 years away depending on the specific domain of the “intelligence” being learned. To be sure, computers trained using Machine Learning hold tremendous promise, as well as the potential for massive disruption in the workplace. But these systems remain a far cry from genuine intelligence. Just ask Apple AAPL -0.14% Siri, and you will see what I mean. The hype around AI, and confusion over what the term actually means, will inevitably lead to some disillusionment as the limitations of this technology become apparent.

With that context in mind, here’s what I expect for the coming year for Machine Learning and AI.

1. Hardware accelerators for Machine Learning will proliferate.

Today, nearly all training of deep neural networks (DNNs) is performed using NVIDIA GPUs. Conversely, DNN inference, or the actual use of a trained network can be done efficiently on CPUs, GPUs, FPGAs, or even specialized ASICs such as the Google TPU, depending on the type of data being analyzed. Both training and inference markets will be hotly contested in 2017, as Advanced Micro Devices GPUs, Intel’s newly acquired Nervana chips, NVIDIA, Xilinx and several startups all launch accelerators specifically targeting this lucrative market. If you would like a deeper dive into the various semiconductor alternatives for AI, please see my companion article on this subject here.

2. Select application domains will leverage Machine Learning to improve efficiency of mission-critical processes.

If you are trying to find the killer AI app, the increasingly pervasive nature of the technology will make it difficult to identify. However, Machine Learning has begun to deliver spectacular results in very specific niches where the pattern recognition capabilities can be exploited, and this trend will continue to expand into new markets in 2017.

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LATEST SCIENCE & TECHNOLOGY NEWS **Kurzwel**

Monday | April 24, 2017
DAILY EDITION
LATEST SCIENCE & TECHNOLOGY NEWS

The first 2D microprocessor — based on a layer of just 3 atoms

April 24, 2017
Overview of the entire chip. AC=Accumulator, internal buffer; PC=Program Counter, points at the next instruction to be executed; IR=Instruction Register, used to buffer data- and instruction-bits received from the external memory; CU=Control Unit, orchestrates the other units according to the instruction to be executed; OR=Output Register, memory used to buffer output-data; ALU=Arithmetic Logic Unit, does the actual calculations. [3] (credit: TU Wien)May one day replace traditional microprocessor chips as well as open up new applications in flexible electronics

Researchers at Vienna University of Technology (known as TU Wien) in Vienna, Austria, have developed the world’s first two-dimensional microprocessor — the most complex 2D circuitry so far. Microprocessors based on atomically thin 2D materials promise to one day replace traditional microprocessors as well as open up new applications in flexible electronics. Consisting of 115 … more…

‘Negative mass’ created at Washington State University

April 21, 2017
Experimental images of an expanding spin-orbit superfluid Bose-Einstein condensate at different expansion times (credit: M. A. Khamehchi et al./Physical Review Letters)Washington State University (WSU) physicists have created a fluid with “negative mass,” which means that if you push it, it accelerates toward you instead of away, in apparent violation of Newton’s laws. The phenomenon can be used to explore some of the more challenging concepts of the cosmos, said Michael Forbes, PhD, a WSU assistant … more…

NEW EVENTS

london_futuristsWho can save Humanity from Superintelligence?

Dates: Apr 29, 2017
Location: London, UK
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ai-summit-logoThe AI Summit San Francisco

Dates: Sep 27 – 28, 2017
Location: San Francisco, California
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