DEVELOPMENT


Microsoft Unveils Azure Databricks, New AI Tools for Developers

@machinelearnbot

To help increase developers' productivity and simplify app development, Microsoft has announced new data platform technologies and cross-platform developer tools. The company launched a new AI-powered platform "Azure Databricks" during an event for developers late Thursday. Designed in collaboration with the founders of Apache Spark, Azure Databricks analytics platform delivers one-click setup, streamlined workflows and an interactive workspace. The platform will enable organisations to provide self-service analytics and machine learning over all data with enterprise-grade performance and governance, Microsoft said in a statement. "With today's intelligent cloud, emerging technologies like Artificial Intelligence (AI) have the potential to change every facet of how we interact with the world," said Scott Guthrie, Executive Vice President.


How will artificial intelligence change healthcare?

#artificialintelligence

When Amazon first came out with a smart recommendation algorithm for customers, millions of consumers receive their first tailored shopping experience personalized to their own interests. This changed the consumer world and introduced us to a whole new era of shopping. Amazon's algorithms, using a method called "item-to-item collaborative filtering", are able to provide targeted shopping recommendations by creating a personalized experience for each person. Even in a very basic form, this was the beginning of using machine learning in a very practical manner. But can such artificial intelligence and machine learning also act as an enabler for changes in medicine and healthcare, as much as Amazon's algorithm changed consumerism?


How to install mxnet for deep learning - PyImageSearch

#artificialintelligence

What I like about mxnet is that it combines the best of both worlds in terms of performance and ease of use. Whenever I'm implementing a Convolutional Neural Network I tend to use Keras first. Keras is less verbose than mxnet and is often easier to implement a given neural network architecture training procedure. But when it's time for me to scale up from my initial experiments to ImageNet-size datasets (or larger) I often use mxnet to (1) build an efficiently packed dataset and then (2) train my network on multiple GPUs and/or multiple machines. Since the Python bindings to mxnet are compiled C/C binaries I'm able to milk every last bit of performance out of my machine(s).


Govt of Karnataka launches centre of excellence for data science and artificial intelligence - ETtech

#artificialintelligence

The Government of Karnataka has launched a Centre of Excellence for data science and Artificial Intelligence (CoE-DS&AI) in collaboration with NASSCOM. To be established with around Rs 40 crore, the CoE will be its first-of-its-kind port based on a public-private partnership model, and will accelerate the ecosystem in Karnataka by providing the impetus for the development of data science and artificial intelligence across the country. NASSCOM will work towards accelerating innovation, enabling industry-oriented research, promoting adoption of data-driven decision making by enterprises and enabling appropriate skills & talent development. The Centre of Excellence will provide high-end technology capabilities, data, expertise, thought leadership, and curated programs to augment capabilities across academia, enterprises, government, and innovators or advanced start-ups. Priyank Kharge, Minister for IT, BT & Tourism, Government of Karnataka, said, "Karnataka has led the IT revolution in India and has always been at the forefront in areas of science and information technology.


Bill Gates: Benefits of robots, healthcare AI will outweigh pitfalls

#artificialintelligence

Speaking at the Misk Global Forum in Riyadh, Saudi Arabia this week, Microsoft co-founder and now billionaire philanthropist Bill Gates shared his thoughts on today's technological advancements, including artificial intelligence (AI). Gates, who has previously warned about the challenges AI could bring, told audiences at a CNBC-moderated panel during the forum that the benefits of AI will far outweigh these potential pitfalls -- particularly in the case of healthcare AI. "We are in a world of shortage, but these advances will help us take on all of the top problems," Gates said, CNBC reports. "We need to solve these infectious diseases … We need to help healthcare workers do their job." Gates also pointed out how AI and robotics will reshape the labor landscape in the developed world. "As we free labor up from things like manufacturing, we can shift it to some of these very human-centric needs," he explained, giving society time to take care of the elderly, for example.


Self Driving Cars, The Most Hyped Thing Since…The Segway?

#artificialintelligence

With headlines like these, it's hard not to get excited about autonomy and self driving cars. After all, we've seen the cars in Minority Report, Total Recall, and iRobot, and thought to ourselves: "When can we finally get into those cars?" Truth be told, it may be quite a while before we're actually there. There's a general misalignment between what the public think is "fully autonomous" versus what these executives are actually saying. Elon Musk's 2018 goal is to have a self driving car that's safer than a human driver.


Will heightened interest in AI accelerate IoT deployments?

#artificialintelligence

"Anyone who wants to be 100% sure, will be 100% late." I heard this sentence recently at the IoT Forum in Munich. It is a very good summary of the feeling that is emerging in the debate on Industry 4.0. The buzzword battles of the past 48 months seem to be just that; buzzword battles. Stakeholders seem to be stuck waiting for the proposition with guarantees.


Data Science of Digital Payments

@machinelearnbot

– Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. Today every company on this planet collect vast quantities of data on a daily basis or even per second. For example credit card issuers with every credit card swipe and completed transaction capture critical customer information, In case of mobile payments/money the same thing happen or even in banks same scenarios. However, the raw data alone does not generate the insights needed to drive business decisions or simply not good enough at all. It's the proper analysis of this data that unlocks its true value.


Karnataka bets big on Artificial Intelligence, Big Data

#artificialintelligence

At a time when technologies like Artificial Intelligence are becoming the new world order, Karnataka is betting big to prepare itself for these new drivers of employment. Drones that monitor crop health, medical devices for early detection of cancer and apps that help visually impaired read and identify objects were some of the AI--based innovations on display at the Bengaluru Tech Summit 2017. Many of these companies pitched their products and services to an audience of top business executives, government officials, and investors at Karnataka government's flagship event held in Palace Grounds here. "We are at the beginning of what is called as fourth industrial revolution," said Kris Gopalakrishnan, co-founder of software giant Infosys. He said multinational companies are setting up research and development facilities here because they are able to find professionals at a scale who understand technologies such as AI and Machine Learning.


Machine learning, containers and DevOps among McKinsey top 10 enterprise infrastructure trends

#artificialintelligence

Machine learning-optimised stacks, container-first architectures and DevOps for both software and hardware are among the key trends redefining enterprise IT infrastructure, according to a new report from McKinsey. The piece, authored by Arul Elumalai, Kara Sprague, Sid Tandon, and Lareina Yee, looks at what is changing and how companies need to fight back. Many of these have frequently been covered by this publication; some, like the public cloud going mainstream, are long overdue. Yet there is an interesting titbit here. Given the long-established leadership of Amazon Web Services (AWS), Microsoft, and Google in public cloud, McKinsey argues that their entrenched dominance will mean only organisations with'significant capital investment capabilities' will be able to compete in future.