Sorry, science fiction fans, but the "replicants" of the Blade Runner saga or the "terminators" of the eponymous action movie franchise are not on the horizon. "Don't imagine human-like, humanoid robots when you think of the future of robots," said Kim Sang-bae, the world-renowned robot scientist who developed a four-legged walking robot called "Cheetah," which has gained widespread media exposure. Not only is it impossible to develop human-like robots now, it may remain impossible in the future, according to Kim, a mechanical engineering professor at the Massachusetts Institute of Technology. While the ultimate stage of robotics may, indeed, be creating machines that can think and work on their own, there is a yawning gap between where robot technologies stand now and that final-stage development. In an interview with Asia Times, Kim predicted that the robot industry would continue to expand by creating robots which can do very specific things better than humans. But he conceded there is a real risk that the social inequalities in the sector will accelerate.

Many may not know this but Dr. Fatmah Baothman is the first woman in Saudi Arabia and the Middle East to hold a PhD in Modern Artificial Intelligence (AI), a milestone for the entire region and definitely a proud achievement for the Kingdom. This week, the Middle East's first female specialist in AI has been awarded the first-ever Women AI Award, which was announced at the VB AI Summit Transform 2019 in San Francisco, United States, according to Saudi Gazette. According to the award's website, this first-of-its-kind award aims to honor changemakers in the field, women leaders paving the way in rethinking process, policy, technology, and education as AI advances. Dr. Baothman was awarded under the category AI Research, which honors a woman whose research in AI has made a significant impact by helping accelerate progress within her organization, as part of academic research, or by influencing approaches to AI technology. As reported by the news site, Dr. Baothman expressed her gratitude in receiving such a global honor and for the recognition women in AI are receiving for their accomplishments.

In almost every field (and especially in any kind of data science), there will be times when we wish to estimate data we don't have by using the data that we do have. Given the plethora of machine learning algorithms and the similarly-daunting number of implementations of these algorithms (in different languages, packages, etc), it can be difficult to know where to start. This post shares my biggest takeaways from diving into using some of these tools, in the context of estimating air pollution from wildfires over the western US. Remotely sensed image of the 2015 Washington Wildfires, showing smoke mixed with cloud cover. Since September 2017 I've worked on the Environmental Health Team in Earth Lab, investigating the impacts of exposure to air pollution from wildfires on human respiratory and cardiovascular health in the western US.

Where once banks and credit unions routinely left technology to specialists, the subject now has become elevated to the highest-ranking issue impacting retail banking. Research by The Economist Intelligence Unit (EIU) for Temenos finds that coping with new technology is the top concern of retail bankers, ahead of changing consumer behavior, political and economic instability and dealing with bad loans, among other factors. No institution can afford to ignore the combination of new competition from fintechs and big technology companies, multiple new technologies, and soaring consumer expectations is bringing unprecedented change to retail banking that And few are ignoring it, as the EIU survey indicates. However, the how quickly and how extensively organizations respond varies sharply by institution and sometimes even by country. In a study of 161 publicly traded banking institutions around the world, Accenture found that just over half are "digital laggards," with no plans to go digital or just "half-hearted efforts."

In the past few years, machine learning (ML) has become commercially successful and AI firmly established as a field. With its success, more attention is being paid specifically to the gender gap in AI. Compared to the general population, men are overrepresented in technology. While this has been the case for several decades, the opposite was true in the early days of computing when programming was considered a woman's job. Diversity has been shown to lead to good business outcomes like improved revenue.

In its rush to solve all the world's problems through deep learning, Silicon Valley is increasingly embracing the idea of AI as a universal solver that can be rapidly adapted to any problem in any domain simply by taking a stock algorithm and feeding it relevant training data. The problem with this assumption is that today's deep learning systems are little more than correlative pattern extractors that search large datasets for basic patterns and encode them into software. While impressive compared to the standards of previous eras, these systems are still extraordinarily limited, capable only of identifying simplistic correlations rather than actually semantically understanding their problem domain. In turn, the hand-coded era's focus on domain expertise, ethnographic codification and deeply understanding a problem domain has given way to parachute programming in which deep learning specialists take an off-the-shelf algorithm, shove in a pile of training data, dump out the resulting model and move on to the next problem. Truly advancing the state of deep learning and way in which companies make use of it will require a return to the previous era's focus on understanding problems rather than merely churning canned models off assembly lines.

Small and medium-sized businesses are the keystone of the modern-day labor market. In the United States alone, small businesses employ almost 50% of the private workforce, and recent data shows that companies with fewer than 20 employees have added 1.2 million net new jobs. But although their growth is vital to a sustainable global economy, SMBs continue to struggle to get the funding they need. The traditional lending system simply isn't set up to meet the smaller capital needs of these types of enterprises: taking into account the risks and the long review process, small business loans typically don't pay off for banks. Chances of being accepted are incredibly low for businesses that aren't already well-established, and they rarely have the structure to carry them through the long review process anyway.

The data science can be defined as the convergence of Computer Science, programming, mathematical modeling, data analytics, academic expertise, traditional AI research, and applying the statistical techniques through scientific programming tools such as Python, R, TensorFlow, Java, on an ecosystem of SQL, NoSQL, GraphDB, streaming computing platforms such as Apache Spark, Apache Kafka, Apache Storm, Apache Nifi, Apache Flink, Apache Geode, and linked data to extract new knowledge discovery through data patterns and provide new insights from distributed computing platform from the tsunami of big data. Though, many times it is possible to define the statistical language models, it's difficult to implement through object-oriented programming languages. Therefore, it is critical to wear the hats of an advanced programmer, infrastructure architect to provide web scale performance with in-memory computing and apply traditional research with machine learning and deep learning algorithms to create novel architectures unique to each enterprise and avoid one-size fits all approach. Real-time analysis is all the rage in the data science industry. Therefore, leveraging in-memory computing ecosystems can provide faster execution results to the corporations.

Check out the full program at the TensorFlow World Conference, October 28-31, 2019. Best price ends August 2. Attention readers: We invite you to access the corresponding Python code and iPython notebooks for this article on GitHub. Given all of the higher level tools that you can use with TensorFlow, such as tf.contrib.learn But often with these higher level applications, you cannot access the little inbetween bits of the code, and some of the understanding of what's happening under the surface is lost. In this tutorial, I'll walk you through how to build a convolutional neural network from scratch, using just the low-level TensorFlow and visualizing our graph and network performance using TensorBoard. If you don't understand some of the basics of a fully connected neural network, I highly recommend you first check out Not another MNIST tutorial with TensorFlow.

The US labor market looks markedly different today than it did two decades ago. It has been reshaped by dramatic events like the Great Recession but also by a quieter ongoing evolution in the mix and location of jobs. In the decade ahead, the next wave of automation technologies may accelerate the pace of change. Millions of jobs could be phased out even as new ones are created. More broadly, the day-to-day nature of work could change for nearly everyone as intelligent machines become fixtures in the American workplace. Until recently, most research on the potential effects of automation, including our own, has focused on the national-level effects. Our previous work ran multiple scenarios regarding the pace and extent of adoption. In the midpoint case, our modeling shows some jobs being phased out but sufficient numbers being added at the same time to produce net positive job growth for the United States as a whole through 2030.