Amazon, DeepMind/Google, Facebook, IBM, and Microsoft today announced that they will create a non-profit organization that will work to advance public understanding of artificial intelligence technologies (AI) and formulate best practices on the challenges and opportunities within the field. Academics, non-profits, and specialists in policy and ethics will be invited to join the Board of the organization, named the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI). The objective of the Partnership on AI is to address opportunities and challenges with AI technologies to benefit people and society. Together, the organization's members will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology. It does not intend to lobby government or other policymaking bodies.

During keyhole surgery, surgeons typically rely on sight as they remotely control the delicate operation. A new robot promises to give them one important extra sense: touch. The invention, revealed Wednesday at the Australasian Simulation Congress in Melbourne, Australia, is called the HeroSurg. Created by a team at Australia's Deakin University, as well as Harvard University, the robot may look a little terrifying but its inventors hope it will make operations safer and more accurate. Unlike most current keyhole surgery tools, the robot uses haptic feedback to deliver a sense of touch to the operator, as well as 3D images so the surgeon can see where their instruments are placed.

Amazon, Google, Facebook, IBM, and Microsoft have announced they are forming a non-for-profit organisation to educate the public about artificial intelligence (AI) technologies, as well as alleviate anxieties around its application. The collective, which includes Google's AI subsidiary DeepMind, also plans to develop best practices on the challenges and opportunities within the field of AI. The organisation, called Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI), will address legal and ethical challenges that AI presents, encourage public discourse, and identify opportunities to use AI to bring improvements to society. The organisation does not intend to be a regulatory body, with a statement saying it does "not intend to lobby government or other policymaking bodies." Members of the partnership will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology.

Five of the biggest tech companies have launched a collaboration to help the public understand the benefits of artificial intelligence. The New York Times reported in early September that Google, Facebook, Microsoft, IBM and Amazon had been meeting to discuss an AI-related project. Their collaboration is officially called "Partnership on Artificial Intelligence to Benefit People and Society," and it has a few goals other than to make sure people know that AI research isn't all about creating killer robots. Partnership on AI will support related research and recommend best practices in ethics, transparency and privacy when it comes to artificial intelligence studies. The project also aims to create an open platform where researchers and major players in the industry can communicate.

Artificial intelligence is becoming ubiquitous. As its reach grows and is engrained into consumer products and services, elements of control and regulation are required. Silicon Valley's biggest companies are joining forces to introduce this. Facebook, Google (in the form of DeepMind), Microsoft, IBM, and Amazon have created a partnership to research and collaborate on advancing AI in a responsible way. Each member of the Partnership on AI will contribute financial and research resources.

As more IT organizations begin to build out their own private clouds, many of them are struggling with I/O contention issues created when different classes of workloads are required to share access to the same storage. Storage administrators are tasked with manually trying to optimize the I/O path for each workload running in the cloud. To help with this issue, Kaminario today unfurled a cloud service that employs machine learning algorithms to identify precisely what I/O contention issues are arising. Once identified, Chris Buckel, technology evangelist for Kaminario says, a storage administrator can then use the information generated by the Kaminario Clarity analytics service to apply the appropriate policies to eliminate that contention. "Clarity is going to provide a suggestion about the best pathway for a particular types of workload," says Buckel.

Data Mining is defined as the procedure of extracting information from huge sets of data. In other words, we can say that data mining is mining knowledge from data. The tutorial starts off with a basic overview and the terminologies involved in data mining and then gradually moves on to cover topics such as knowledge discovery, query language, classification and prediction, decision tree induction, cluster analysis, and how to mine the Web. This tutorial has been prepared for computer science graduates to help them understand the basic-to-advanced concepts related to data mining. Before proceeding with this tutorial, you should have an understanding of the basic database concepts such as schema, ER model, Structured Query language and a basic knowledge of Data Warehousing concepts.

In blue we show the recurrent connections – the output'm' at time (t – 1) is fed back to the memory at time't' via the three gates; the cell value is fed back via the forget gate; the predicted word at time (t – 1) is fed back in addition to the memory output'm' at time't' into the Softmax for tag prediction. In spite of this fact, when we test images with multiple clothing type, our trained model generates tags for these unseen test images quite accurately ( 80% accurate). Prediction accuracy of our model improves quickly with increasing number of training iterations and stabilizes after about 20,000 iterations. Moreover, combining DCNN-RNN model helps us extend the trained model to solve completely different problem like fashion image tag generation.

The industry is falling over itself to make products'intelligent' but what does that really mean to a user? As a Product Manager in this space, I've been thinking about intelligence and what it really means to users. There are many definitions of intelligence but they tend to be technical definitions of AI rather than intelligence seen through the eyes of a user. As we progress through the stages, what users once considered as intelligent, can become ordinary over time. For example, improving search so that it was accurate and fast could be regarded as highly intelligent at the time but now it's the minimum we expect.

The tech giants have joined forces to create a new AI partnership to study and formulate best practices on AI technologies, and to serve as an open platform for discussion and engagement about AI and its influences on people and society. Google's involvement in the project is mainly through its research subsidiary DeepMind, which made news recently when its AI machine beat a world-class human player in the Asian board game, 'Go'. The heavyweights lobby says, the goal is to "conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology".