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.