Dipayan Dev Dipayan Dev has completed his M.Tech from National Institute of Technology, Silchar with a first class first and is currently working as a software professional in Bengaluru, India. He has extensive knowledge and experience in non-relational database technologies, having primarily worked with large-scale data over the last few years. His core expertise lies in Hadoop Framework. Dr. Hadoop has recently been cited by Goo Wikipedia in their Apache Hadoop article. Apart from that, he registers interest in a wide range of distributed system technologies, such as Redis, Apache Spark, Elasticsearch, Hive, Pig, Riak, and other NoSQL databases.

The workplace is going to look drastically different ten years from now. The coming of the Second Machine Age is quickly bringing massive changes along with it. Manual jobs, such as lorry driving or house building are being replaced by robotic automation, and accountants, lawyers, doctors and financial advisers are being supplemented and replaced by high level artificial intelligence (AI) systems. So what do we need to learn today about the jobs of tomorrow? The robots and computers of the future will be based on a degree of complexity that will be impossible to teach to the general population in a few short years of compulsory education.

NEC's artificial intelligence platform is mixing data, bus telematics, and human observation to determine whether a bus driver in Singapore is likely to cause an accident in the next three months. NEC, in partnership with Singapore public transport service provider SMRT Corporation, is using artificial intelligence (AI) to prevent bus accidents on the city-state's roads. The Japanese giant is taking historical data from a bus driver's work records, telematics data produced by each bus, and observations made by on-board data scientists to determine whether a driver is likely to cause an accident in the next three months, and intervene before SMRT has to deal with the costly and potentially life-threatening aftermath. Here's what Samsung's latest budget phones may tell us about the Galaxy S9 Addressing the Tech Leaders Forum 2017 on Monday, Mervyn Cheah, head and vice president of NEC Laboratories Singapore, explained that once an at-risk driver is identified, they are sent for further training. Buses on the road, sometimes they do create accidents and actually they will always react when they have an accident, you'll send them for training, he said.

Low paying jobs seem to be more at risk of a robot takeover, but a new developed has suggests that not even highly paid Wall Street jobs are safe. Goldman Sacs has published a job posting seeking a software developer to build a'robot adviser' that provides mass affluent clients with'detailed information on their financial portfolio and analytics'. The move comes as Goldman is looking at ways to broaden its customer base outside the super wealthy, including making deeper inroads into new consumer-focused businesses. Goldman Sacs has published a job posting seeking a software developer to build a platform that gives mass affluent clients'detailed information on their financial portfolio and analytics' According to the job posting, Goldman Sachs wants to leverage'a global technology platform offering an integrated suite of tools and applications to service clients. 'Digital innovations to shape client experience and enable our Institutional and Third-Party Distribution (TPD) salesforce with the tools required to best serve our clients,' the listing reads.

This is another installment of Mighty AI's "Conversations in Machine Learning" blog series. Each week, our content human, Cassie, shares a summary of a recent conversation we had with a machine learning team and potential customer--what they're building, how they're handling training data today, etc. Read more about the series here. Each of those would be appropriate depending on the context--you'd connect its brand name to a search engine (because it is one, among other things), you'd call it a technology company with a glance-over of its corporate site, and you'd say it's a machine learning company if you dug into its products. That's because these days, machine learning is powering virtually everything the company does and is behind nearly everything it builds. To back up a bit, this is a European technology firm with deep roots in search and respectably deep roots in machine learning, too (employees there developed a proprietary ML methodology nearly a decade ago, and creating a whole new methodology sounds impressive to me at least).

In this special guest feature, Elliott Yama, Chief Data Analyst at Apttus discusses the ways that machine learning is impacting the B2B customer experience – where the evolution mirrors the initial advancement of B2C E-Commerce. Elliott leads the team responsible for the company's artificial intelligence and machine learning work. Machine learning is a hot term right now – and deservedly so. Over the last few years it has significantly advanced how sales and revenue operations function. However, it's often used as a guiding tool for salespeople – helping them to determine correct pricing, bundling and more in the name of progressing their deals.

TensorFX is an end to end application framework to simplifies machine learning with TensorFlow - both training models and using them for prediction. It is designed from the ground up to make the mainline scenarios simple with higher level building blocks, while ensuring custom or complex scenarios remain possible by preserving the flexibility of TensorFlow APIs. Simple, consistent set of usage patterns Local or cloud, single node or distributed execution, in-memory data or big data sharded across files, you should have to write code once, in a single way regardless of how the code executes. A Toolbox with Useful Abstractions The right entrypoint for the task at hand, starting with off-the-shelf algorithms that let you focus on feature engineering and hyperparam tuning. If you need to solve something unqiue, you can focus on building TensorFlow graphs, rather than infrastructure code (distributed cluster setup, checkpointing, logging, exporting models etc.).

Competition amongst universities is set to increase with institutions closely differentiating themselves to attract and retain the best quality students, academics and staff. Key to this differentiation will be an extensive technology adoption and innovation strategy, enhancing the student experience, delivery of learning content, community engagement and campus management. The education technology (Edutech) market in Australia is expected to grow significantly amidst increasing student demand for education services and technology innovation, competition amongst institutions and decreasing acquisition costs. Frost & Sullivan anticipates that as the learning experience becomes increasingly digitised, technologies and solutions incorporating big data and analytics, collaboration, Augmented / Virtual Reality technology, Artificial Intelligence and learning management systems will play a key role within universities in the coming years. Frost & Sullivan's most recent analysis, Australian Edutech Market: Key Trends, Technologies and Opportunities 2016-2022 finds that the Australian Edutech Market is expected to grow to AUD 1.7 Billion by 2022.

Until now, humans have held sway as the most dominant force on Earth. Despite lacking some of the physical attributes of a host of other species, we have conquered the planet by virtue of our mighty minds. Yet by 2025, a computer costing as little as $1,000 will have the equivalent processing speed of the human brain, according to Silicon Valley engineer and entrepreneur Peter Diamandis. And artificial intelligence systems are already faster and more accurate than humans when searching vast databases for anomalies or patterns in customer behaviour. They can also "learn" from what they have discovered and react to those findings.

Thanks to both a range of demanding new applications, such as Artificial Intelligence (AI), Natural Language Processing (NLP) and more, as well as a perceived threat to Moore's Law (which has "guided" the semiconductor industry for over 50 years to a state of staggering capability and complexity), we're starting to see an impressive range of new output from today's silicon designers. Entirely new chip designs, architectures and capabilities are coming from a wide array of key component players across the tech industry, including Intel (NASDAQ:INTC), AMD (NASDAQ:AMD), Nvidia (NASDAQ:NVDA), Qualcomm (NASDAQ:QCOM), Micron (NASDAQ:MU) and ARM, as well as internal efforts from companies like Apple (NASDAQ:AAPL), Samsung (OTC:SSNLF), Huawei, Google (NASDAQ:GOOG) (NASDAQ:GOOGL) and Microsoft (NASDAQ:MSFT). It's a digital revival that many thought would never come. In fact, just a few years ago, there were many who were predicting the death, or at least serious weakening, of most major semiconductor players. Growth in many major hardware markets had started to slow, and there was a sense that improvements in semiconductor performance were reaching a point of diminishing returns, particularly in CPUs (central processing units), the most well-known type of chip.