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  • Radha Roy

    Country Head

    I long for the raised voice, the howl of rage or love.

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    GM,India

    Contented with little, yet wishing for much more.

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    Branch Head, Banglore

    If anything is worth doing, it's worth overdoing.

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Showing posts with label automation. Show all posts
Showing posts with label automation. Show all posts
  • Self-driving car that uses IBM's AI unveiled

    IBM today took the wraps off its first big foray into the world of self-driving cars, not as the driver of them, but as the brain behind making your self-driving journey a little more interesting.
    IBM Watson, the company’s AI platform, is powering services in Olli — an electric-powered vehicle that can carry up to 12 people designed by Local Motors, a car maker based out of Arizona that uses newer technologies like 3D printing to bring down the cost of making cars on a low-volume basis.
    The cars will start operations first in Washington, DC, before expanding to deployments in Miami-Dade County and Las Vegas later this year. IBM says Miami-Dade County will run a pilot to transport people around Miami using these autonomous vehicles.
    Local Motors and IBM, along with Intel, have worked together before, specifically on its Rally Fighter concept car. It looks like Olli might be the first commercial product that has resulted from the partnership.
    Olli will be using a special version of Watson aimed at automotive applications and it is not fully powering the car’s self-driving features. Instead it’s aimed at “improving the passenger experience,” according to a statement from IBM.
    “IBM technology, including IBM Watson or IBM Watson IoT technology, does not control, navigate or drive Olli. Rather, the IBM Watson capabilities of Olli will help to improve the passenger experience and allow natural interaction with the vehicle,” the company said.
    While companies as diverse as tech giants like Google, as well as auto giants like GM, are all laying down bets on self-driving vehicle strategies, it’s interesting that IBM — one of the world’s biggest and most iconic technology companies, and home to an AI platform (Watson) that is extending into so many different areas of life — is not doing more to develop the technology that will power the driving of these vehicles. 


    Or, at least, it is not publicly discussing anything like that at this point. When we first got wind of this deal back in January, we’d heard it involved IBM working on self-driving technology, although we didn’t hear more beyond that. IBM on a more wider scale has been putting itself in front of the car industry by doing things like partnering with and sponsoring the organization that runs the big auto show in Detroit in January.
    Local Motors, another partner of IBM, says it sees Olli as its first step, which could be a clue into what will come next:
    "Olli offers a smart, safe and sustainable transportation solution that is long overdue,” John B. Rogers, co-founder of Local Motors, said in a statement. “Olli with Watson acts as our entry into the world of self-driving vehicles, something we’ve been quietly working on with our co-creative community for the past year. We are now ready to accelerate the adoption of this technology and apply it to nearly every vehicle in our current portfolio and those in the very near future. I’m thrilled to see what our open community will do with the latest in advanced vehicle technology.”
    There are four Watson APIs that Olli will use: Speech to Text, Natural Language Classifier, Entity Extraction and Text to Speech, and among the functions that the vehicle will be able to perform as a result include analyzing high volumes of transportation data from 30+ sensors on the vehicle.

    "Passengers will be able to interact conversationally with Olli while traveling from point A to point B,” IBM says, “discussing topics about how the vehicle works, where they are going, and why Olli is making specific driving decisions.” Other features will be asking Olli for suggestions of restaurants to eat or local landmarks. But not driving itself.

    "IBM is excited to work with Local Motors to infuse IBM Watson IoT cognitive computing capabilities into Olli, exploring the art of what’s possible in a world of self-driving vehicles and providing a unique, personalized experience for every passenger while helping to revolutionize the future of transportation for years to come,” said Harriet Green, General Manager, IBM Watson Internet of Things, Commerce & Education, in a statement.

    "Improving the sustainability of local transportation networks as part of a wider goal to create more vibrant, livable, sustainable cities within Miami-Dade County, and improve the quality of life for residents is our top priority,” said Miami-Dade County Mayor Carlos Gimenez, in a statement. “We must do more to improve transit and mobility in our community and the deployment of autonomous vehicles is a big step in the right direction.”
    To know more must watch the following video:

  • Tesla Model 3 Reached 400,000 Preorders

    Tesla CEO Elon Musk revealed this morning that the company has received almost 400,000 preorders for its new Model 3 sedan. Musk unveiled the Model 3 earlier this month to much interest, and 325,000 preorders after just a week. "We are now almost at 400,000 orders for the Model 3," Musk told a Norwegian conference this morning, according to Reuters. Interest in the Model 3 has been impressive, and "surprised even us" said Musk.



    Hundreds of Tesla fans lined up outside dealerships before the car was even officially unveiled, and Tesla received more than twice the Model 3 preorders it expected. Tesla's Model 3 is set for release in 2017, priced starting at $35,000 with at least 215 miles of range. The huge amount of preorders could cause availability issues next year, and Musk also expects the Model 3 to reach 500,000 preorders before more details about the car are revealed later this year.

    Watch Highlight of Tesla Model E Launch Event Here



  • Amping antimicrobial discovery with automation

    The antimicrobial arsenal that we count on to save millions of lives each year is alarmingly thin--and these microbes are rapidly evolving resistance to our weapons. But help may be on the way: In a study posted in the AMB Express, researchers from the National Institute of Standards and Technology (NIST) show that automated techniques commonly used to screen new drugs for mammalian cell toxicity could also dramatically speed up the challenging task of antimicrobial discovery.
    In the age-old struggle between humans and microbes, bacteria seem to be regaining the offensive. Only around a dozen classes of chemicals protect us from the myriad pathogens that populate our environment. Numerous agencies, including the World Health Organization and the Centers for Disease Control and Prevention, have recently warned that evolved resistance could soon render common antibiotics useless, and that few replacement drugs are in the pipeline.
    The shortage of new antimicrobials is not a result of scientists lacking candidate chemicals. The fungal and plant worlds abound with potential antimicrobials, and chemists concoct new synthetic molecules all the time. However, a major bottleneck occurs at the lab bench. Any candidate compound must be tested at multiple concentrations against multiple strains of bacteria in different forms. This remains a cumbersome process, with numerous time- and labor-intensive steps that lab workers must currently carry out by hand.
    But NIST researcher Samuel Forry and colleagues are convinced that the process could be vastly sped up using automation. To do so, Forry and his team looked to one of the pharmaceutical industry's most powerful tools: high-throughput screening. For several decades, companies have routinely used automated systems to test potential drugs' effects on mammalian cells in culture. In these studies, robots prepare samples of cells in arrays of small plastic wells, inject measured amounts of drugs and test whether cells live or die. The method can quickly assess multiple chemicals at different concentrations, all in parallel and with minimal human intervention.
    High-throughput screening has seen limited use for antimicrobial discovery, Forry says, because less research and development money is available and because of the large variation among microbial populations and growth conditions. Hoping to stimulate the field, Forry and his team adapted a high-throughput screening robot for antimicrobial testing. The researchers tested a set of antimicrobial compounds known as pyridinium salts against the common bacterium Streptococcus mutans, which causes tooth decay.
    Part of the challenge in identifying useful antimicrobial compounds is that chemicals that kill free-swimming cells are often less effective against the same bacteria growing in biofilms like the plaque that can form on teeth. So Forry's team used automation to culture both free-swimming cells and biofilms, as well as an intermediate state, side-by-side in 96-well plates. The researchers measured antimicrobial activity in three different ways by identifying the concentrations that reduced bacterial activity by half, that prevented any detectable activity, and that entirely killed the bacteria. They determined the drugs' effects with high throughput by measuring light passing through the wells or using chemicals that change color to indicate metabolic activity.
    The team found that the automated system delivered results indistinguishable from those obtained by doing the experiments by hand. More importantly, the robot took only a third as much time as humans do, freed up laboratory personnel for other tasks, and carried out the procedures without errors. "That's a huge improvement from the point of view of laboratory workflow and a great boon for people trying to identify and characterize antimicrobials," Forry says.
    The trials weren't fully automated--for instance, the researchers moved samples from the incubator to the screening robot by hand--but Forry says his team has demonstrated the concept, and existing technology can fill in the remaining steps. He expects other research labs will adopt the technology first, followed by pharmaceutical companies. "Once a number of people start to use this and find that it works for them as well as it has worked for us, I could easily see companies and contract labs doing it."
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