Monthly Archives: August 2017

Google has become very interested in artificial intelligence in recent years, and particularly its applications for regular people. For example, here's a load of experiments that it's running involving machine learning.

Now, however, researchers at the Texas Advanced Computing Center have shown how artificial intelligence techniques can also deliver better search engine results. They've combined AI, crowdsourcing and supercomputers to develop a better system for information extraction and classification.

At the 2017 Annual Meeting for the Association of Computational Linguistics in Vancouver this week, associate professor Matthew Lease led a team presenting two papers that described a new kind of informational retrieval system.

"An important challenge in natural language processing is accurately finding important information contained in free-text, which lets us extract it into databases and combine it with other data in order to make more intelligent decisions and new discoveries," Lease said.

"We've been using crowdsourcing to annotate medical and news articles at scale so that our intelligent systems will be able to more accurately find the key information contained in each article."

They were able to use that crowdsourced data to train a neural network to predict the names of things, and extract useful information from texts that aren't annotated at all.

In the second paper, they showed how to weight different linguistic resources so that the automatic text classification is better. "Neural network models have tons of parameters and need lots of data to fit them," said Lease.

In testing on both biomedical searches and movie reviews, the system delivered consistently better results than methods that didn't involve weighting the data.

"We had this idea that if you could somehow reason about some words being related to other words a priori, then instead of having to have a parameter for each one of those word separately, you could tie together the parameters across multiple words and in that way need less data to learn the model," said Lease.

He added: "Industry is great at looking at near-term things, but they don't have the same freedom as academic researchers to pursue research ideas that are higher risk but could be more transformative in the long-term."

More here:

How artificial intelligence can help deliver better search results - TechRadar

In this sponsored post,Ramnath Sai Sagar, Marketing Manager at Mellanox Technologies, explores how recent advancements in Artificial Intelligence, especially deep learning, are set to make an impact in the field ofastronomy and astrophysics.

Ramnath Sai Sagar, Marketing Manager at Mellanox Technologies

Since the dawn of the space age, unmanned spacecraft have flown blind, with little to no ability to make autonomous decisions based on their environment. That, however, changed in the early 2000s, when NASA started working on leveraging Artificial Intelligence (AI) and laying the foundation that would help Astronauts and Astronomers to work more efficiency in Space. In fact, just last month, NASAs Jet Propulsion Laboratory published how AI will govern the behavior of space probes.

Recent advancements in Artificial Intelligence, especially Deep Learning (a subfield in AI), are set to make a deeper impact in the field of astronomy and astrophysics. From navigating the unknown terrain of Mars, to analyzing petabytes of data generated from Square Kilometer Array, to finding Earth-like planets in our messy galaxy, AI is already revolutionizing our lives here on earth by building smarter and more autonomous cars, helping us find solutions to climate change, revolutionizing healthcare and much more. Mellanox is proud to be working closely with the leading companies and research organizations to make advancements in the field of Artificial Intelligence and Astronomy.

AI: The Next Industrial Revolution

Coined in 1956 by Dartmouth Assistant Professor John McCarthy, AI existed before the Race to Space but could only deliver rudimentary displays of intelligence in specific context. Progress was limited due to the complexities of algorithms needed to tackle various real-world issues. Many were above the ability of a mere human to execute. This however, changed in the past decade mainly due to two reasons:

Due to this, AI now presents one of the most exciting and potentially transformative opportunities for the mankind. In fact, in some quarters it is being heralded as the next industrial revolution:

The last 10 years have been about building a world that is mobile-first. In the next 10 years, we will shift to a world that is AI-first. Sundar Pichai, CEO of Google, October 2016

AI for the Messy Galaxy

While humanity has made great strides in exploring the observable universe, we need to rely on intelligent robots to explore where we cannot humanly go. This is because our galaxy, the Milky Way, is one messy place, filled with cosmic dust from stars, comets, and more; concealing the very things scientists want to study. That said, there are three major challenges in leveraging AI in the future of space exploration. Firstly, the probes will have to be able to learn about and adapt to unknown environments including responding to thick layers of gas in a planets atmosphere, extreme temperatures or unplanned for fluctuations in gravity.

Secondly, when a probe falls outside the communication range, would have tofigure out when and how to return the data collected during the time the signal was lost. Finally, given the vast distances in space, it could take several generations before the probe reaches its destination and therefore, will need to be flexible enough to adapt to any new discoveries and innovations we make here on earth. The solution to these problems will require training AI models on petabytes of data captured using supercomputers.

The benefits of using AI to control space-exploring robots are already being realized by missions that are currently underway. For example, Opportunity, the Mars Exploration Rover, which was launched back in 2003, has an AI driving system called Autonav that allows it to explore the surface of Mars. In addition, Autonomous Exploration for Gathering Increased Science (AEGIS) has been used by the NASA Mars rover, Curiosity, since May in order to select which aspects of Mars are particularly interesting and subsequently take photos of.

Image Captured by AEGIS Enabled Curiositys ChemCam.

But Mars is by no means the final destination and the exploration of more challenging destinations will require even more advanced AI. For example, exploring the subsurface ocean of the Jovian moon Europa in the hope of finding alien life, will require bypassing a thick (~10km) ice crust. Controlling this exploration would be severely limited without advanced autonomy.

Artificial Intelligence Needs Intelligent Network

Since the early age of Mellanox, we have been working closely with NASA and many research labs help solve the challenges of scientific computing, whether its the aerodynamic simulation of the Jet Propulsion Engine or monitoring the universe in unprecedented detail. In addition, over the last few years, Mellanox has also enabled the pioneers in the field of AI including Baidu for their advancements in autonomous cars and Yahoo for image recognition. The applications of autonomous driving and object recognition go far beyond the limits of Earth and Mellanox is proud to be working closely with several research organizations and companies and helping them achieve technological breakthroughs in the field of astronomy and astrophysics.

Forty-eight years ago, Neil Armstrong said Thats one small step for man, one giant leap for mankind, when he became the first human to set the foot on the surface of the moon. The next giant leap for mankind will come from the small step of a robot, powered by AI and Mellanox.

Ramnath Sai Sagar is Marketing Manager at Mellanox Technologies. This post originally ran as part of Mellanox Technologies Interconnected Planet blog series.

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Artificial Intelligence: A Journey to Deep Space - insideHPC

Spend enough time on Facebook, and youll likely encounter a post written in a tongue thats foreign to you. Thats because the social network has two billion users and supports over 45 languages. On Thursday, Facebook announced that all of its user translation servicesthose little magic tricks that happen when you click see translation beneath a post or commentare now powered by neural networks, which are a form of artificial intelligence.

Back in May, the companys artificial intelligence division, called Facebook AI Research, announced that they had developed a kind of neural network called a CNN (that stands for convolutional neural network, not the news organization where Wolf Blitzer works) that was a fast, accurate translator. Part of the virtue of that CNN is that instead of looking at words one at a time, it can consider groups of them.

Now, Facebook says that they have incorporated that CNN tech into their translation system, as well as another type of neural network, called an RNN (the R is for recurrent). Those RNNs, Facebook said in a blog item about the news, are better at understanding the context of the whole sentence than the previous system, and can reorder sentences as needed so that they make sense.

The upshot? Facebook says that the new AI-powered translation is 11 percent more accurate than the old-school approach, which is what they call a phrase-based machine translation technique that wasnt powered by neural networks. That system translated words or small groups of words individually, and didnt do a good job of considering the context or word order of the sentence.

As an example of the difference between the two translation systems, Facebook demonstrated how the old approach would have translated a sentence from Turkish into English, and then showed how the new AI-powered system would do it. The first Turkish-to-English sentence reads this way: Their, Izmirs why you said no we dont expect them to understand. Now check out the newer translation: We dont expect them to understand why Izmir said no. Notice how the AI fixed the mistakes in word and phrase order?

While neural networks had been working together with the more traditional translation system before today, now all the translation gets its smarts from AI. This new system is capable of translating in 2,000 directions. For example, a translation from English to French is one direction, French to English is a second, and French to Italian is a third direction, and so forth. Astoundingly, the neural networks handle 4.5 billion translation per day, making them quite the linguists.

Excerpt from:

Artificial intelligence now powers all of Facebook's translation - Popular Science

Mashable

Artificial intelligence proves that craft beer names are total nonsense Mashable If you're a craft beer connoisseur or even just an occasional drinker you've likely noticed that names for new brews are getting out of hand. Likely in order to distance themselves from traditional, European beer names such as Franziskaner Royal ... and more »

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Artificial intelligence proves that craft beer names are total nonsense - Mashable

There is no doubt that AI (artificial intelligence) is the new electricity and everyone is trying to get benefits from the trend. Many companies are integrating AI solutions in their business operationsto reap the benefit of emerging machine learning (ML) technologies. The seamless introduction of AI, however, requires thoughtful adaptation of corporate strategy to requirements of this emerging technology.As a partner at a venture studio, I see companies try to get in the trenches of machine intelligence without the proper preparation. Here's what we recommend companies to advance their initiatives effectively.

To be efficient, data that is fed into ML algorithms should be properly labeled, cleaned up and structured. Companies produce huge amounts of unstructured data that adds no value unless necessary transformations are made. To succeed in improving their data, companies have the option of in-house data labeling and data cleansing or using third-party services of companieslike Scale that offer programmatic access to a growing community of people specializing in making data usable.

Similarly, to enhance their AI preparedness, companies need to integrate their dispersed data sources into the unified data warehousing framework. Data warehouses and data marts allow storing data generated by different business operations and departments in one place and in the uniform representation. This allows for centralized sourcing of data for ML algorithms. Even though it sounds like a simple exercise, most of the companies we work with have trouble organizing their data sources.

Prioritize and Grow Narrow Expertise

It is often tempting to use AI solutions in every business process that may benefit from automation and ML. However, such strategy leads to dispersion of organizational resources and decreases the cumulative effect of AI innovation.

Instead of creating a horizontal platform for AI innovations, companies should prioritize concrete AI solutions that have the biggest potential to increase financial value and customer satisfaction. Growing narrow expertise in one specific area will help concentrate organizational resources on one particular task, ultimately contributing to the development of a more general solution for your business.

Get Advantage of the Academia

Academia is the main breeding ground of expertise and skills in emerging technologies like AI. The depth of theoretical knowledge and expertise offered by AI researchers is hard to attain in the private sector.

Therefore, each company that we partner with is trying to find its own machine intelligence expertise to boost its strategy. We recommend these companies reach out to talent in academia. Academic AI experts can offer a long-term AI agenda for your company. breathing life in the most exciting and revolutionary ideas that would otherwise be lost in the lengthy articles published in academic journals.

In turn, companies should provide AI researchers with an opportunity to share their research with the public by encouraging them to publish scientific papers, participate in conferences, and maintain a connection with universities. AI researchers will join those companies that offer more freedom and necessary organizational resources to put their theoretical ideas into practice.

Create a Process Versus Chaotic Experimentations

AI experimentation is great. However, too many companies rush into new AI domains without putting structured approach in place. Treating AI innovation as a process starts from automating existing data analytics procedures to create a pipeline of fresh data. Without automation of existing operations, new AI solutions may reach the wrong conclusions simply because they work on out-of-date data.

The integrity of the AI process requires modernization of the entire IT infrastructure, ranging from in-house servers and databases to cloud-based services and networks. Sound AI innovation process may be also facilitated by the organizational change towards multi-disciplinary teams and training employees to new roles associated with the AI innovation. With all departments of your organization prepared for the technological disruption, AI integration will be closely aligned with the corporate strategy and organizational goals.

Global leaders of the AI innovation facilitate the fast adoption of AI technology in all industries by open-sourcing ML libraries and APIs (Application Programming Interface). Such ML libraries as Google TensorFlow offer companies access to out-of-the-box algorithms and neural networks optimized for fast deployment in the enterprise setting.

Businesses can also take advantage of cloud-based ML APIs that allow to easily bootstrap in-house AI software. One example of such APIs is Googles Cloud Vision API provided as part of Google Cloud offerings. The system encapsulates powerful machine learning models for image classification, object detection, image-to-text transformation provided as REST API. The system may be used by companies for building metadata of their image catalogs, moderating offensive content, and developing new marketing strategies based on image sentiment analysis.

Similar functionality is also available in the recently released TensorFlow Object Detection API. Apple has recently joined the party by unveiling its Core ML API that may be used to integrate fast ML algorithms on iPhones, iPads and Apple Watch. Companies using these solutions will have instant access to image and face recognition, and natural language processing in their applications.

Partner atColab, helping startups build tech products.

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5 Ways to Advance Your Machine Learning Initiatives - HuffPost