Rana el Kaliouby will present a keynote, Why AI needs emotion, at the O'Reilly Artificial Intelligence Conference, September 26-27, 2016, in New York. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. While I was in Beijing for Strata Hadoop World, several people reminded me of the chatbot Xiaoice--one of the most popular accounts on the Chinese social media site Weibo. Developed by Microsoft researchers, Xiaoice comes with a personality and is able to engage users in extended conversations on Weibo.

One day the AIs are going to look back on us the same way we look at fossil skeletons on the plains of Africa. An upright ape living in dust with crude language and tools, all set for extinction. Ex Machina, a Hollywood blockbuster made on a 15 million budget, tells the story of a programmer who is invited by his employer, the eccentric billionaire Nathan Bateman who built a fictional search engine called Blue Book, to administer the Turing test to an android with artificial intelligence, which essentially determines whether a computer can trick a human into believing she is having a conversation with another human. Everything in our online life is indexed. Every idle tweet, status update, or curious search query feeds the Google database.

Macquarie Bank is hoping to differentiate itself from rivals in the fiercely competitive digital banking space by using machine learning and natural language processing tools to give its customers detailed insight into their finances. The bank last week unveiled its new online and mobile banking platform for personal banking customers, a vastly significant upgrade from the online-only platform customers have been using to date. It's quite a few years late to market with such an offering, but decided to forego the competitive advantage that comes with being a first mover to wait until the stars aligned with its core banking overhaul and until it could deliver what it considers a truly differentiating offer. In designing the new platform - which consists of native iOS and Android apps and a website - the bank said it looked to customer experiences delivered by the likes of Netflix, Facebook and Spotify to create an offering that would compete with "the last app a customer used". As a result, Macquarie Bank personal banking customers can now use tools underpinned by machine learning and natural language processing technologies to tag and track transactions, automatically categorise spending, and "search-how-you-speak", among other things.

Optunity is a free software package dedicated to hyperparameter optimization to automatically find suitable hyperparameters for a given learning task. Optunity's dedicated optimizers are a drop-in replacement for grid search, which will yield better hyperparameters while requiring less computation time. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in popular machine learning environments, such as scikit-learn, OpenCV and Spark's MLlib.

Four UC Berkeley researchers developed a program to help grade papers during their time working as teaching assistants – and now, they've added artificial intelligence to their app to help instructors speed up the grading process. The team launched the online grading app Gradescope two years ago and have accumulated 10 million answers to around 100,000 questions from a wide range of college courses – the app has already shortened the grading process by 50 percent due to its friendly interface and the ability for multiple teaching assistants to grade papers in parallel. Their new AI features addresses three challenges: identify question types, distinguishing between different written marks, and recognizing handwriting. AI helps turn grading into an automated, highly repeatable exercise by learning to identify and group answers, and thus treat them as batches. The addition of AI promises to slash grading times by as much as 90 percent, said Sergey Karayev, a Gradescope co-founder who finished his PhD in computer science in 2014.

Data feedback loops are critical for machine learning platforms according to research firm Wikibon. In his latest research Professional Alert, an elaboration on the role of digital business platforms in machine learning explored in his previous Alert, "Digital Business Platform for Machine Learning Apps", Wikibon Big Data & Analytics Analyst George Gilbert takes a close look at the data feedback loops that are central to machine learning. He finds that data wrangling and defining the variables or features that drive the model are critical to establishing successful analysis and should be entrusted to data scientists. The data that feeds machine analysis often comes from sources that aren't curated, and that means that preparing data requires human expertise. Defining the correct variables and data features with which to separate valuable data from noise is critical.

Last year, McKinsey launched a multi-year study to explore the potential impact of automation technologies on jobs, organizations and the future of work. "Can we look forward to vast improvements in productivity, freedom from boring work, and improved quality of life?," its initial article on the study asked, or "Should we fear threats to jobs, disruptions to organizations, and strains on the social fabric?" Most jobs involve a number of different tasks or activities. Some of these activities are more amenable to automation than others. But just because some of the activities have been automated, does not imply that the whole job has disappeared. To the contrary, automating parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as by enabling them to focus on those aspects of the job that most need their attention.

Humanoid robots still have problems staying upright, especially in tricky situations, but it's evident that they're making some progress. IHMC has posted a video showing Boston Dynamics' Atlas robot balancing on one foot on the edge of a plywood board about 0.8 inches thick. The feat is a "lucky run," IHMC admits (it's rare that the robot stays poised for so long), but it's relatively effortless. The worst you see before the fall is shaking as IHMC's algorithm sometimes makes poor estimates of the robot's state. As brief as the balancing act is, it's telling.

Data Science sits at the core of any analytical exercise conducted on a Big Data or Internet of Things (IoT) environment. Data science involves a wide array of technologies, business, and machine learning algorithms. The purpose of data science is just not doing machine learning or statistical analysis but also to derive insights out of the data that a user with no statistics knowledge can understand. In a fast paced environment such as Big Data and IoT where the type of data might vary over the course of time, it becomes difficult to maintain and recreate the models each and every time. This gap calls up for an automated way to manage the Data Science algorithms in those environments.

Smart home dream has already become a reality for many average Americans. According to one survey, 28 percent of U.S. adults and 47 percent of millennials have smart home products in their home. Of those who already have home automation gadgets, 81 percent are likely to purchase a home with smart technology already installed. Now, with public interest gaining momentum, smart home technology is poised to go far beyond smart sensors and individual gadgets. The future of the connected home is quickly becoming all about seamless integration, convenience, sustainability, and automation that can interact with personal health and living preferences to improve our lives.