Mark Twain famously said "There are three kinds of lies: lies, damned lies, and statistics." Maybe if Mark Twain were alive today, he'd add "alternative facts" to that list. BusinessWeek in the article "Lies, Damn Lies, and Financial Statistics" reminds us of the management challenges with statisticians or "data scientists" who manipulate the data to create "pseudo-facts" that can lead to sub-optimal or even dangerously wrong decisions. My University of San Francisco class recently did a hackathon with a local data science company. It was insightful for all involved (and I learned of a new machine learning tool – BigML – which I will discuss in a future blog).

Race does not stand up scientifically, period. To begin with, if race categories were meant primarily to capture differences in genetics, they are doing an abysmal job. The genetic distance between some groups within Africa is as great as the genetic distance between many "racially divergent" groups in the rest of the world. The genetic distance between East Asians and Europeans is shorter than the divergence between Hazda in north-central Tanzania to the Fulani shepherds of West Africa (who live in present-day Mali, Niger, Burkina Faso, and Guinea). Armed with this knowledge, many investigators in the biological sciences have replaced the term "race" with the term "continental ancestry." This in part reflects a rejection of "race" as a biological classification. Every so-called race has the same protein-coding genes, and there is no clear genetic dividing line that subdivides the human species.

Follow the steps below to understand the algorithm - Create duplicate copies of all independent variables. When the number of independent variables in the original data is less than 5, create at least 5 copies using existing variables. Shuffle the values of added duplicate copies to remove their correlations with the target variable. It is called shadow features or permuted copies. Combine the original ones with shuffled copies Run a random forest classifier on the combined dataset and performs a variable importance measure (the default is Mean Decrease Accuracy) to evaluate the importance of each variable where higher means more important.

Like the hackathon, the code challenge is to build a machine learning based signal classifier that can be used for observations made in real time at the Allen Telescope Array (ATA). The winning classifier of the code challenge will be implemented at the ATA and become part of the data analysis pipeline. The challenge will last from June 1 to July 31st. Submitted entries will be judged based on their classification accuracy, accuracy for low-amplitude signals, and speed of single event classification. More details will be provided later about how to submit your work. THIS IS THE EVENTBRITE FOR THE HACKATHON ONLY! YOU MUST REGISTER FOR THE CODE CHALLENGE SEPARATELY The hackathon in San Francisco is a kick-off event for the code challenge (See below for info on the separate prizes being awarded for each event.)

Last year a former product team peer referred Datorama to connect with me. Usually I don't review vertical industry analytics solutions but the marketing angle was appealing to me. Secret be told…I fell in love with data back in the early 90s during my marketing major courses…marketing research, statistics and statistical process control. I am an American Marketing Association member and have always truly relished marketing throughout my technical career. After a briefing with Datorama, I was shocked to learn how many data sources the average marketer today has to aggregate in reporting.

The webinar also discussed how large corporates and innovative and fast-moving startups can work together in this process in order to help customers create new service-based business models. The webinar was held on May 30th 2017.

The robot revolution may not have replaced us yet, but automation is undoubtedly creeping its way into many careers. Hundreds of jobs now require some level of robotics skills, a new analysis from career website Zippia found. Nearly 1,000 job titles now have robotics-related requirements, the study found, which it defined as degree of autonomy, a set of intended tasks, and ability to function without human intervention. Of 985 such jobs it found in its database, 492 job titles were listed multiple times in fields that had robotics-related requirements. The manufacturing industry has already been hit by automation, which has impacted employment in the U.S. auto industry.

Let's talk about some global AI trends, use cases for various industries and its benefits for your business. As interest is growing every day, I'm getting the same questions again and again at deepPiXEL. Briefly, deepPiXEL provides AI solutions focused on text-based conversations for businesses in finance, retail and telecom industries. Over the past few months, we have talked to over 200 companies in these and other industries. Today, I'd like to pass on some of the knowledge and insights we have gleaned from these conversations.

The company said it posted new advanced driver system wins, forged alliances with new partners and moved forward with its proprietary high-definition mapping project by signing definitive collaboration deals with several major automakers.

Talk of mass job losses and the need to give humans a computer layer in their brain as a result of artificial intelligence (AI), an idea put forward by billionaire Elon Musk, are "alarmist" and distract from the good being done by the technology, a top start-up CEO told CNBC on Thursday. Major warnings have been issued by technologists about the impact of AI on society in the next few years. For example, Alibaba founder Jack Ma said society could face decades of "pain" from the result of automation, while Musk has started a company called Neuralink to research the development of human-machine interfaces. Babak Hodjat, the CEO of Sentient Technologies, one of the world's highest-funded AI start-ups, however, said that such developments were still in the realm of science fiction and in fact are distracting companies from developing world-changing solutions. "We are nowhere near that on the technology side, and it's distracting to the fact that AI today can help the world in so many places, and the discourse is being taken over by folks that are alarmists, around something that might happen in 100 years, 150 years," Hodjat told CNBC during an interview at the Pioneers tech show in Vienna on Thursday.