To create one such talk script, follow the steps given below:-Steps

1. Open Notepad.
2. Copy and paste the exact code given below.

          Dim Message, Speak
          Message=InputBox("Enter text","Speak")
          Set Speak=CreateObject("sapi.spvoice")
          Speak.Speak Message

    3. Click on File Menu, Save As, select All Types in Save as Type option, and save the file as Speak.vbs or "*.vbs".

    4.  Double click on the saved file, a window will open like the one in the image. Enter some text in enter text column and click OK.

Now your Computer will speak / talk what you typed in Step 4. Try it yourself.

Windows Compatibility: This VBS file can be executed on all versions of Windows including Windows XP,Windows Vista, Windows 7 and Windows 8.

Personal Experience: I personally showed this trick to many of my friends and they were literally left awestruck. You too can impress your friends by making your PC talk and be the Computer Wiz.

Bedier’s new company, Poynt, has a new kind of credit-card terminal. The sleek-looking, $299 touch-screen gadget accepts cards with embedded chips—which will soon be widespread—as well as digital payments, and can run apps for things like customer loyalty programs and sales analytics. Poynt unveiled the device on Wednesday and plans to ship it to merchants in early 2015.

Google’s digital payment app, Google Wallet, offers consumers a number of payment-related features, including a quick way to pay at stores by tapping a phone that contains a near-field communication (NFC) chip. Although adoption of Google Wallet has been slow, NFC technology is gaining in popularity, and that is likely to accelerate with the introduction of a similar system from Apple, called Apple Pay (see “With Apple Pay, Forget Cash, Just Pull Out Your Phone”).

Meanwhile, the U.S.’s major credit-card companies are mandating a shift to more secure credit cards that eschew the familiar magnetic strip for a chip that uses a unique string of numbers for each transaction (a standard known as EMV, which stands for “Europay, Mastercard, and Visa” for the card companies that first backed the technology).

This change, which has already occurred in other parts of the world and aims to staunch the kinds of security breaches that have occurred at major retailers like Target, Neiman Marcus, and Home Depot, means that U.S. merchants must upgrade millions of credit-card terminals to accept the new cards by October 2015 or face liability for any fraudulent transactions.

Bedier, a former PayPal executive who came to Google in 2011, saw an opportunity to switch his focus from the gadgets we can use to make payments to the ones used to handle the transactions: credit-card terminals and cash registers.

“No matter how great the value proposition, you can’t force people to install new technology if it’s a ‘nice to have,’” Bedier says. “People only change technology when they have to.”

Poynt’s terminal is dominated by two touch screens that meet at an angle—a seven-inch display that a store employee will use to ring up sales and a 4.3-inch display facing the opposite direction that a customer will peer at to see details of the transaction and sign their approval. The apex of the device houses a slot for dipping a credit card, and there’s a built-in receipt printer that will spit out paper from an opening below the customer touch screen.

The Poynt device, Bedier says, is meant to accept all kinds of payments that may become increasingly popular in the coming years. It accepts payments via NFC (used by services such as Google Wallet and Apple’s new Apple Pay) and QR code. It includes Bluetooth as well.

Bedier showed me how it works during an interview conducted via Skype video. The Poynt terminal he used said “Welcome to Main St. Bakery” on the customer screen, and showed a few different payment options. Merchants could use the screen for ads or store specials when not taking payments, Bedier says.

Bedier had a colleague come and make a purchase using Apple Pay on an iPhone: Bedier typed in the amount due and the colleague tapped the terminal with his iPhone while placing his finger on the iPhone’s home button—which on newer models is also a fingerprint reader—to validate his identity.

The Poynt terminal weighs a little over a pound and contains a wireless modem and eight-hour rechargeable battery, which means it could be carried around a store or restaurant if needed. It will come with three apps, including a cash register app and an app that lets merchants pick out insights about their sales, such as what’s selling best and worst.

In addition to convincing merchants to pay $299 for the device (Bedier contends this is about what they’d pay for one of the new card terminals anyway), Poynt will have to get developers interested. Bedier says the company won’t make money from sales of the device itself, or a cut of payment fees. Instead, it plans to take a cut of revenue from sales of apps that will run on the device.

This means Poynt is largely dependent on the success of developer apps for its survival. On Wednesday, it showed off its first steps toward corralling developers by naming six partners including Intuit, Vend, and Kabbage, and releasing a software development kit in hopes of attracting other developers, too.

That’s why academic and corporate labs have been experimenting with “neuromorphic” chips modeled on features seen in brains. These chips have networks of “neurons” that communicate in spikes of electricity (see “Thinking in Silicon”). They can be significantly more energy-efficient than conventional chips, and some can even automatically reprogram themselves to learn new skills.

Now a neuromorphic chip has been untethered from the lab bench, and tested in a tiny drone aircraft that weighs less than 100 grams.

In the experiment, the prototype chip, with 576 silicon neurons, took in data from the aircraft’s optical, ultrasound, and infrared sensors as it flew between three different rooms.

The first time the drone was flown into each room, the unique pattern of incoming sensor data from the walls, furniture, and other objects caused a pattern of electrical activity in the neurons that the chip had never experienced before. That triggered it to report that it was in a new space, and also caused the ways its neurons connected to one another to change, in a crude mimic of learning in a real brain. Those changes meant that next time the craft entered the same room, it recognized it and signaled as such.

The chip involved is far from ready for practical deployment, but the test offers empirical support for the ideas that have motivated research into neuromorphic chips, says Narayan Srinivasa, who leads HRL’s Center for Neural and Emergent Systems. “This shows it is possible to do learning literally on the fly, while under very strict size, weight, and power constraints,” he says.

The drone, custom built for the test by drone-maker company Aerovironment, based in Monrovia, California, is six inches square, 1.5 inches high, and weighs only 93 grams, including the battery. HRL’s chip made up just 18 grams of the craft’s weight, and used only 50 milliwatts of power. That wouldn’t be nearly enough for a conventional computer to run software that could learn to recognize rooms, says Srinivasa.

The flight test was a challenge set by the Pentagon research agency DARPA as part of a project under which it has funded HRL, IBM, and others to work on neuromorphic chips. One motivation is the hope that neuromorphic chips might make it possible for military drones to make sense of video and sensor data for themselves, instead of always having to beam it down to earth for analysis by computers or humans.

Prototypes made under DARPA’s program—like HRL’s—have delivered promising results, but much work remains before such technology can perform useful work, says Vishal Saxena, an assistant professor working on neuromorphic chips at Boise State University. “The biggest challenge is identifying what the applications will be and developing robust algorithms,” he says.

Researchers also face a chicken-and-egg scenario, with chips being developed without much idea of what algorithms they will run and algorithms being written without a firm idea of what chip designs will become established. At the same time, neuroscientists are still discovering new things about how networks of real brain cells work on information. “There’s a lot of work to be done collectively between circuit and algorithm experts and the neuroscience community,” says Saxena.

Still, HRL’s owners, GM and Boeing, are already considering how they might commercialize the technology, says Srinivasa. One option could be to use neuromorphic chips to build a degree of intelligence into the sensors increasingly found in cars, planes, and other systems.

The forecasts are helping power companies deal with one of the biggest challenges of wind power: its intermittency. Using small amounts of wind power is no problem for utilities. They are accustomed to dealing with variability—after all, demand for electricity changes from season to season, even from minute to minute. However, a utility that wants to use a lot of wind power needs backup power to protect against a sudden loss of wind. These backup plants, which typically burn fossil fuels, are expensive and dirty. But with more accurate forecasts, utilities can cut the amount of power that needs to be held in reserve, minimizing their role.

Before the forecasts were developed, Xcel Energy, which supplies much of Colorado’s power, ran ads opposing a proposal that it use renewable sources for a modest 10 percent of its power. It mailed flyers to its customers claiming that such a mandate would increase electricity costs by as much as $1.5 billion over 20 years.

But thanks in large part to the improved forecasts, Xcel, one of the country’s largest utilities, has made an about-face.

It has installed more wind power than any other U.S. utility and supports a mandate for utilities to get 30 percent of their energy from renewable sources, saying it can easily handle much more than that.

An early version of NCAR’s forecasting system was released in 2009, but last year was a breakthrough year—accuracy improved significantly, and the forecasts saved Xcel nearly as much money as they had in the three previous years combined. This year NCAR is testing a similar forecasting system for solar power.

Mining these detailed forecasts to develop a more flexible and efficient electricity system could make it much cheaper to hit ambitious international goals for reducing carbon emissions, says Bryan Hannegan, director of a $135 million facility at the National Renewable Energy Laboratory (NREL) in Golden, Colorado, that uses supercomputer simulations to develop ways to scale up renewable power. “We’ve got a line of sight to where we want to go in the long term with our energy and environment goals,” he says. “That’s not something we’ve been able to say before.”

Chasing the Wind

No one is more aware of the challenges of integrating wind power into the grid than Dayton Jones, a power plant dispatcher for Xcel Energy. From his perch on the 10th floor of the Xcel building in downtown Denver, he’s responsible for keeping the lights on in Colorado. Doing so requires matching power production to electricity demand by turning power plants on and off and controlling their output. Generating too much or too little power can damage electrical appliances or even plunge the grid into a blackout. Wind power, with its sharp fluctuations, makes his job harder.

A few years ago, dispatchers like Jones couldn’t trust forecasts of how much wind power would be available to the grid at a given time. Those forecasts were typically off by 20 percent, and sometimes wind power completely failed to materialize when predicted. The solution was to have fossil-fuel plants idling, ready to replace all of that wind power in a few minutes. This approach is expensive, and the more the system is intended to rely on wind power, the more expensive it gets. What’s more, running the backup fossil-fuel plants means you’re “throwing carbon up into the sky,” says William Mahoney, deputy director of the Research Applications Laboratory at NCAR. “It costs money, and it’s bad for the environment.”

NCAR’s forecasts give Jones enough confidence in wind power to shut down many of the idling backup plants. The number varies depending on the certainty of the forecast. If the weather is cold and wet and there’s a chance ice could form on wind turbines and slow them down or stop them from spinning, he might need enough fossil-fuel backup to completely replace his wind power.

But on nice days with steady, abundant wind, he might shut down all his fast-response backup plants, even those normally reserved for responding to changes in demand. Under such circumstances, Jones can use the wind farms themselves to ensure that power supply matches demand: the output of a wind turbine can be changed almost instantly by angling the blades so they capture more or less wind. Computers at Xcel’s building in Denver tell wind farms how much power to produce, and automated controls coördinate hundreds of turbines, changing output minute by minute if needed.

Xcel’s original forecasts used data from just one or two weather stations per wind farm. Now NCAR collects information from nearly every wind turbine. The data feeds into a high-resolution weather model and is combined with the output from five additional wind forecasts. Using historical data, NCAR’s software learns which forecasts are best for each wind farm and assigns different weights to each accordingly. The resulting über-forecast is more accurate than any of the original ones. Then, using data about how much power each turbine in the field will generate in response to different wind speeds, NCAR tells Xcel how much power to expect, in 15-minute increments, for up to seven days.

Forecasting solar power is next for NCAR and Xcel, but that can be even trickier than wind. For one thing, Xcel doesn’t get information about how much power private rooftop solar panels are generating, so it doesn’t know how much of that power it could lose when clouds roll in. NCAR’s new solar forecasts will use data from satellites, sky imagers, pollution monitors, and publicly owned solar panels to infer how much solar power is being generated and then predict how that amount will change.

Virtual Energy

How might extremely accurate wind and solar forecasts help us use enough renewable energy to reach climate goals of significantly reducing carbon dioxide emissions? Researchers at NREL’s new Energy Systems Integration Facility start by looking at how well wind and solar power can offset each other. To what extent, for example, can wind blowing at night make up for the lack of sunshine? But they are also looking at how to couple forecasts with smart dishwashers, water heaters, solar-panel inverters, water treatment plants, and electric-car chargers, not only to accommodate shifts in the wind but to ride out inevitable windless periods and weeks of cloudy weather without resorting to fossil fuels.

Take the example of electric cars. A car stores enough electricity to power a house for anywhere from half a day to several days, depending on the size of the battery pack. And it has sophisticated power electronics that can control the timing and vary the rate of charging, which could offer a way to match fluctuating wind power to electricity demand. With small modifications, the cars’ batteries can deliver stored power to a home and to the power grid. There aren’t many electric cars now, but that could easily change in the decades it will take before renewable energy makes up more than 30 or 40 percent of the electricity supply (wind supplies 4 percent now, and solar less than 1 percent).

At NREL, researchers can plug 30 electric cars into docks that let them interface with power-grid simulations on a supercomputer, to project what would happen if thousands of cars were connected to the grid. The idea is that electric cars might store power from solar panels and use it to power neighborhoods when electricity demand peaks in the evening, and then recharge their batteries using wind power in the early morning hours.

Forecasts like the ones being developed at NCAR will be “absolutely critical,” says Bri-Mathias Hodge, a senior research engineer at NREL. They will help determine when the cars’ batteries should charge to maximize the electricity they make available to the grid without leaving drivers short of the power they need.

Even before that becomes a reality, though, forecasts from NCAR are already having a big effect. Last year, on a windy weekend when power demand was low, Xcel set a record: during one hour, 60 percent of its electricity for Colorado was coming from the wind. “That kind of wind penetration would have given dispatchers a heart attack a few years ago,” says Drake Bartlett, who heads renewable-energy integration for Xcel. Back then, he notes, they wouldn’t have known whether they might suddenly lose all that power. “Now we’re taking it in stride,” he says. “And that record is going to fall.”

—Kevin Bullis