The bio industry is evolving and tech will play a large part in bio's next chapter. In fact, biology is looking more and more like programming lately, from "digital therapeutics" to "computational biomedicine" and "cloud bio". But how can this help with, for example, cancer? And how does it affect investing in bio startups (hint: software lets you de-risk at every stage). This interview of a16z bio fund general partner Vijay Pande (conducted by David Clark of VenCap International) discusses the challenges -- and possibilities -- of software brought to healthcare startups, including the difference between computer science x bio (as opposed to the'biotech' of yore) and, therefore, why now.

Increasingly becoming a necessity in Ecology and Earth Science research, handling complex data can be a tough nut when traditional statistical methods are applied. As its first publication, the new technologically-advanced Open Access journal One Ecosystem features a review paper describing the benefits of using machine learning technologies when working with highly-dimensional and non-linear data. Natural sciences, such as Ecology and Earth science, focus on the complex interactions between biotic and abiotic systems in order to infer understand these systems and make predictions. Traditional statistical methods can impose unrealistic assumptions that result in unsound conclusions as the era of'big data' meets ecology and earth science. Machine-learning-based methods, capable of inferring missing data and handling complex interactions, are more apt for handling complex scientific data.

Splunk announced new versions of Splunk Enterprise, Splunk IT Service Intelligence (ITSI), Splunk Enterprise Security (ES) and Splunk User Behavior Analytics (UBA). These products leverage machine learning to speed-up and facilitate extracting insights from machine-generated data. Splunk was founded in 2003 and brought "big data" to Wall Street's attention with its 2012 IPO. It has always focused on machine-generated data and its platform captures, indexes and analyzes real-time data in a searchable repository, on premises or in the cloud. Machine learning extends the Splunk platform by adding outlier and anomaly detection, adaptive thresholding and predictive analytics capabilities, applying over 25 commonly-used machine learning algorithms or custom algorithms to build data models that forecast future events.

Imagine one day walking into a gorgeous structure--like LA's famous Walt Disney Concert Hall--only to discover it was designed by a computer system and constructed by automated robotic arms. Ai Build, a London-based startup, aims to pave the way to 3D printing on large scales. The company is equipping industrial-grade Kuka robotic arms with artificial intelligence and "3D printing guns" to 3D print large structures that focus on maximizing efficiency with labor and materials. Founder and CEO Daghan Cam dreamed up the technology while considering traditional commercial construction and wondering what a more efficient and automated process might look like. In October, the company partnered with engineering consulting firm Arup Engineers to debut the 3D printed "Daedalus Pavilion" at the GPU Technology Conference in Amsterdam.

The world of computing has shifted to new platforms and services--like mobile and the cloud--and is promising to head in even bolder new directions, such as virtual reality and augmented reality. So, how does one of the biggest names in computing respond to a changing landscape? To find out, The Wall Street Journal's editor in chief, Gerard Baker, spoke with Microsoft Chief Executive Officer Satya Nadella. Here are edited excerpts of their conversation. BAKER: Your customers are happy.

SEOUL-- Samsung Electronics Co. is looking to give its Siri-like virtual assistant a big promotion. The South Korean smartphone giant is considering a hardware revamp for its next flagship smartphone that could add a dedicated button for an improved artificial-intelligence feature, people familiar with the matter said, as it seeks to woo smartphone users after a disastrous product recall last month. The latest internal prototypes of the premium Galaxy S8 handset include a button on the side edge of the smartphone that would be used to launch a beefed-up virtual assistant based on artificial intelligence, akin to Apple Inc.'s Siri, according to these people, who cautioned that the prototypes aren't final and could change. Samsung will have more time than usual to tinker with the Galaxy S8 after it was forced to discontinue its premium Galaxy Note 7 smartphone last month, following reports of phones catching fire. The embarrassing fiasco damaged the company's brand and engineers still don't know exactly what caused the devices to overheat.

Machine learning is a scientific method that involves analysing data by automating the process. It seeks to answer the very question of'How we can teach our systems to automatically learn and improve with experience?' By learning, it means recognising complex patterns and making intelligent decisions based on data inputs which are often too complex for a human to process. Algorithms are used iteratively to learn from data to find hidden insights in order to bring about reliable and repeatable results. As today's new computing technologies become complex, the science of machine learning is gaining momentum, helping businesses apply complex mathematical calculations to big data – with speed, precision and accuracy.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data. There are a whole swaths (not swatches) of techniques that have been developed over the years like Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN).

Feifei Li, an associate professor at the University who helped develop the program, said it provides a real-time window into how the public is reacting to political events. "What's cool is that you can actually adjust the lens of the window. If you look at the last few months of data altogether, the sentiments for Democrats is stronger than the sentiments for Republicans," Li said. "Given the recent outburst of email scandals, things might change a little bit." Researchers said the biggest surge of positive tweets for Republicans came during the national convention, and again after a video was released of Trump boasting about sexually assaulting women.

Chatbots, AI and Machine Learning pave a new domain of possibilities in the Fashion industry, from Data Analytics to Personal Chatbot Stylists. Chatbots, the automated and smart contextual messaging systems are all in trend from Facebook's annual developers conference, that will allow developer's to develop bots on messengers. With time chatbots are proving to be the future of commerce and the next generation of user interface. Chatbots are entering almost every customer service oriented industries like E-commerce, Healthcare, Travel and Hospitality, Fashion and where not. Chatbots help brand's customers to have a flawless experience.