Systems & Languages


The Scalable Neural Architecture behind Alexa's Ability to Select Skills : Alexa Blogs

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Alexa-like voice services traditionally have supported small numbers of well-separated domains, such as calendar or weather. In an effort to extend the capabilities of Alexa, Amazon in 2015 released the Alexa Skills Kit, so third-party developers could add to Alexa's voice-driven capabilities. We refer to new third-party capabilities as skills, and Alexa currently has more than 40,000. Four out of five Alexa customers with an Echo device have used a third-party skill, but we are always looking for ways to make it easier for customers to find and engage with skills. For example, we recently announced we are moving toward skill invocation that doesn't require mentioning a skill by name.


Inception Network Deep Learning Architecture

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Inception Network Deep Learning Architecture Sharing some of my experience on simplifying the cost & how to go with best combination of hyper parameters.


Linked List Data Structure using Python Udemy

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Get your team access to Udemy's top 2,500 courses anytime, anywhere. If you have started using Python, by now you must have come to know the simplicity of the language. This course is designed to help you get more comfortable with programming in Python. It covers completely, the concept of linked list using Python as the primary language. You need to be equipped with the basics of Python such as variables, lists, dictionary and so on.


Sustainable Deep Learning Architectures require Manageability

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This is a very important consideration that is often overlooked by many in the field of Artificial Intelligence (AI). I suspect there are very few academic researchers who understand this aspect. The work performed in academe is distinctly different from the work required to make a product that is sustainable and economically viable. It is the difference between computer code that is written to demonstrate a new discovery and code that is written to support the operations of a company. The former kind turns to be exploratory and throwaway while the the latter kind tends to be exploitive and requires sustainability.


Import AI: #90: Training massive networks via 'codistillation', talking to books via a new Google AI experiment, and why the ACM thinks researchers should consider the downsides of research

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Training unprecedentedly large networks with'codistillation': …New technique makes it easier to train very large, distributed AI systems, without adding too much complexity… When it comes to applied AI, bigger can frequently be better; access to more data, more compute, and (occasionally) more complex infrastructures can frequently allow people to obtain better performance at lower cost. One limit is in the ability for people to parallelize the computation of a single neural network during training. To deal with that, researchers at places like Google have introduced techniques like'ensemble distillation' which let you train multiple networks in parallel and use these to train a single'student' network that benefits from the aggregated learnings of its many parents. Though this technique has shown to be effective it is also quite fiddly and introduces additional complexity which can make people less keen to use it. New research from Google simplifies this idea via a technique they call'codistillaiton'.


Global Bigdata Conference

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Blockchain is a technology that everybody seems to think will revolutionize the global economy. If nothing else, the cryptocurrency boom has produced a flood of VC money that's trying to cash in on every potential application for this distributed hyperledger technology. It's no surprise that the artificial intelligence (AI) community is also trying to board the blockchain train. Blockchain as an AI compute-brokering backbone: AI developers need the ability to discover, access, and consume distributed computing resources when preparing, modeling, training, and deploying their applications. The Cortex blockchain allows users to submit bids, in the form of AI "smart contracts," for running AI algorithms in a distributed, trusted on-demand neural-net grid.


Invacio Invest ICO – We are working to resolve some of the world's most complex and recalcitrant problems using our original distributed artificial intelligence systems

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The following Agreement is split into two elements: (i) a "Subscription Agreement" relating to the sale of Invacio Tokens (Block-chain Tokens), referred to as'Coins' or'Invacio Coins'; and (ii), a second element relating to the'Gifting' of Invacio Holdings (UK) Ltd C-Class Stock ("Class C Shares", "Class C" or "C shares") allocations via their current Offshore Holding Corporation Invacio (AAP) Holdings Ltd, The Share Gifting is Equity in the the Main UK Limited Company, by William J D West, CEO of Invacio, thus it's holding companies and subsidiaries, Enterprises or Ventures are included in the Gifting as full assets of Invacio Holdings (UK) Ltd .



Probabilistic Graphical Models Coursera

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Stanford University is one of the world's leading teaching and research universities. Since its opening in 1891, Stanford has been dedicated to finding solutions to big challenges and to preparing students for leadership in a complex world.


Continuously Learning and Reinventing, This Man is Connecting Everything to the Internet - THINK Blog

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Dinesh Verma is an IBM Fellow, the company's pre-eminent technical distinction granted in recognition of outstanding and sustained technical achievements and leadership in engineering. Dinesh has worked in IBM Research for nearly 25 years, holds more than 150 patents, is a member of the IBM Academy of Technology, and heads a team that is focused on Distributed Artificial Intelligence (AI). The IBM THINK Blog caught up with Dinesh recently to talk about his current work, as well as his career at IBM. The following is an excerpt and is part of our Perspectives series featuring stories by and about IBMers who take the "long view." THINK: Can you tell us a little bit about your role at IBM? Dinesh Verma: I lead the Distributed AI team at IBM Research at the Thomas J. Watson Research Center in Yorktown, NY.