With great power comes the responsibility to discern when and how to use such power. As we enter an age where the stuff of speculative science fiction--specifically Artificial Intelligence (AI)--becomes reality, climbing the mountain "because it's there" is no longer a sufficient motivational and ethical framework for our future. In order to create a future where essential human values such as autonomy and personal privacy remain vital, we must act with awareness and forethought. Fortunately, the same tech giants who are dedicated to developing sophisticated AI recognize the potential for profound societal shifts the technology will produce. A recent article in The New York Times highlights how thought leaders from Alphabet, Amazon, Facebook, IBM, and Microsoft want "to ensure that A.I. research is focused on benefiting people, not hurting them."

Believe it or not, but a recent study conducted by researchers at the University of Texas and Cornell University has revealed that [PDF] the blurring technology is soon to become exploitable if not obsolete. We may not enjoy full confidentiality that we do now by blurring pictures or license plates for much long because now computer devices can decode images using Artificial Intelligence. This means it is quite easy to unblur pixelated or blurred pictures using various readily and easily available software tools that help in identifying faces or information. The team of researchers utilized a range of deep learning tools to unblur 71% of the blurred faces and numbers, while the percentage increased to 80% when they allowed the computer to guess for up to 5 times. It is true that their algorithm cannot create the original image but it can identify whatever can be seen in the blurred photograph according to the information that it has acquired.

Back in 2012, a team at Google built a state-of-the-art artificial intelligence network and fed it ten million randomly selected images from YouTube. The computer churned through them, and announced that it kept finding these strange things with furry faces. It had, in other words, discovered cats. Artificial intelligence has, all of a sudden, become the next big thing again. It is not so much sweeping across our world as seeping into it, with a combination of enormous computing power and the latest'deep learning' techniques promising to give us better medical diagnoses, better devices, better recipes and better lives.

Driven by a veritable tsunami of data and bolstered by powerful analytics tools, the artificial intelligence solution is on the rise in the enterprise space. AI innovations are driving new efficiencies and a wealth of other positive metrics across a range of industries. In its latest survey of 235 business executives, Narrative Science took a deep look at the adoption of artificial intelligence in the enterprise. The resulting report details the emergence of AI as a core business strategy, with 38 percent of organizations saying they already use AI technologies in the workplace, and 62 percent likely to be using the technology by 2018. The study found AI manifesting in a number of different forms in the enterprise, including deep learning, natural language generation and recommendation engines.

Drones and robots are getting computer vision with higher-resolution cameras and artificial intelligence to recognize objects and images. Many are made with developer boards like Nvidia's Jetson TX1, which provides the smarts for auto-navigation and collision avoidance. TX1 has the horsepower to process live image feeds, and software tools to instantly analyze and provide context to visuals. The TX1 is now a lot faster and better equipped to handle AI and image processing. Nvidia's new Jetpack 2.3 software tools for TX1, announced on Tuesday, are a major update that doubles the deep-learning performance of the board.

Analyze internal and external data through advanced analytics uncovering key relationships and trends from relevant information.Recommend solutions to improve the ability for the business to make optimized, real-time, and predictive based business decisions fostering a new level of intelligence and drive quantified business value.• Highly trained statistical / operations research analyst in analytical disciplines (linear, mixed linear, constraint programming, modeling, simulation, time series analysis, pattern recognition, queuing theory, multivariate analysis, and other predictive analytics techniques.Requirements• Candidates with Singapore working experience highly preferred• Master's degree in Statistics or Operations Research• 5 years experience in Natural Language Processing (NLP)• 5 years experience in data mining, statistical modeling or optimization projects using tools such as SPSS, R, SAS, iLog• 5 years experience in working on a variety of advanced analytics projects with good understanding of the different stages of an analytics project• 2 years experience in working with databases, data mapping, data validation and data transformation• Strong experience in Unstructured Information Management Architecture, Computational Linguistics, and Machine Learning.• Strong knowledge and hands-on experience in Big data analytics and related open-source or commercial platforms is preferred.•

We need the following manual steps to complete the data factory.

When industry observers reference "smart manufacturing," Industry 4.0 or the Industrial Internet of Things (IIoT), it is often related to connecting plant floor equipment and analyzing Big Data in an effort to increase quality and efficiency on the production line or in the supply chain. But sometimes it is the most simplistic applications that make the biggest difference. Enter Otto Motors, a start-up division of Clearpath Robotics, which is focused exclusively on intelligent self-driving vehicles (SDVs) for material transport in factory and distribution facilities. The company and its products, including the Otto 100 for light-load material transport and the Otto 1500 for heavy-load transport, were unveiled this week at the International Manufacturing Technology Show (IMTS) in Chicago. The company launched in April of this year to focus on bringing modular SDVs that use onboard sensors to navigate the facility.

June 1, 2016, The Internet of Things topped the target list for developers working with artificial intelligence across a wide spectrum of technologies including machine learning, neural networks, deep learning, and pattern recognition, according to Evans Data's just released Global Development Survey. While targets for these technologies remain fragmented, IoT was the top target for all of them and in most cases the only target with a double digit response. Non-computer related professional, scientific and technical services was cited second as a target for the above disciplines, and was first in the category of Natural Language Processing. "All the related disciplines that are commonly lumped together as artificial intelligence are being stimulated by the burgeoning growth of Internet of Things," said Janel Garvin, CEO of Evans Data. "These technologies are being incorporated very rapidly into the design and development process across a host of industries, and types of applications, but it's IoT that is the strongest driver."

Machine learning used to take place behind the scenes: Amazon mined your clicks and purchases for recommendations, Google mined your searches for ad placement, and Facebook mined your social network to choose which posts to show you. But now machine learning is on the front pages of newspapers, and the subject of heated debate. Learning algorithms drive cars, translate speech, and win at Jeopardy! What can and can't they do? Are they the beginning of the end of privacy, work, even the human race?