I thought a little deviation from my usual output was well overdue. This week saw Microsoft step into the world of API integration with the release of Microsoft Flow. Flow bridges APIs from different companies allowing data to flow from one company to another. For example, you may want to take your tweets from Twitter and save them to Dropbox. Normally, this would require you as an end user to learn programming, however, Flow has already done the coding allowing you just to provide your credentials and some configuration.

It's a long established tradition for startup founders to fudge their numbers, exaggerate projections, and cherrypick data in meetings with investors. But Venrock health investor Bob Kocher says this approach won't fly with him. "I hear spin every day. I believe I'm lied to more often in Silicon Valley than at the White House," says Kocher, who formerly worked as a special assistant to President Obama to help shape the Affordable Care Act. "I'm looking for entrepreneurs who will level with me."

Nice; however, I see also 3D printing along with machine learning being part of any cosmetic procedures and surgeries. With an ever-increasing volume of electronic data being collected by the healthcare system, researchers are exploring the use of machine learning--a subfield of artificial intelligence--to improve medical care and patient outcomes. An overview of machine learning and some of the ways it could contribute to advancements in plastic surgery are presented in a special topic article in the May issue of Plastic and Reconstructive Surgery, the official medical journal of the American Society of Plastic Surgeons (ASPS). "Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision-making," write Dr. Jonathan Kanevsky of McGill University, Montreal, and colleagues. They highlight some key areas in which machine learning and "Big Data" could contribute to progress in plastic and reconstructive surgery.

Artificial intelligence has never been as pervasive as it is today. From Google's self-driving cars from to Hilton's new Watson-powered hotel concierge, we are witnessing an explosion of AI capabilities. But while it may appear that machines are taking over, they are still tied to their human masters for one very important task: training. "We're in the middle of the'Big Bang' moment of AI," NVIDIA's Senior Product Manager Will Ramey says in the AISummit's new ebook on the topic. "We now have the deep neural networks, the explosion of big data, and now thanks to the leap in processing power with enhanced GPUs, we have the full package to see a real shift in the development of commercial real-world AI applications."

The rich and famous Elon Musk has launched a beta version of a gym for the training of AI systems. These include robots of all kinds. The gymnasium comes under the rubric of his non-profit agency. Termed the OpenAI Gym, it had its ribbon-cutting ceremony a few days ago. The gym provides AI developers with a workstation from which they can apply their algorithmic technology to enhance its worth and caliber. Among the environments offered by this cyber-gym are robots with simulation and even Atari games platforms.

Situated at the intersection of food and technology, NotCo (The Not Company) used artificial intelligence to create the world's smartest chef. Nicknamed Guiseppe, the advanced algorithm examines the molecular structures of meat and dairy products and attempts to replicate them using plant material. The company says the secret to creating notably good-tasting vegan meats, eggs, milk and cheeses is to find plants that have similar molecular structures. The final product should have almost identical flavors and textures. When Guiseppe creates a recipe, the algorithm not only considers the relative availability of various plants, it also tries to enhance nutrition by including additional sustainable ingredients.

Some of the most intelligent minds in the country are taking a closer look at artificial intelligence, and if the student surpasses the master in this scenario, the world will have some pretty impressive machines on its hands. The Intelligence Advanced Research Projects Activity (IARPA), a government institution that concerns itself with large-scale research efforts, has awarded a 28 million grant to Harvard's John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard's Center for Brain Science (CBS), and its Department of Molecular and Cellular Biology. The sizable grant to these three departments is meant to help the famed Boston institution develop "advanced machine learning algorithms by pushing the frontiers of neuroscience." The goal of the latest research effort is to better understand the human brain so that machines may emulate our biological capabilities. Today, our minds and our machines each excel at two differing functions -- while humans are able to quickly recognize patterns and learn, machines are far better at processing large amounts of data.

The International Business Communication Standards (IBCS or IBCS Standards) are practical proposals for the design of reports and presentations, meaning, in most cases, the proper conceptual, perceptual and semantic design of charts and tables.

It then presents this activity to human analysts, who confirm which events are actual attacks, and incorporate that feedback into its models for the next set of data. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx have developed an AI platform called AI2 that predicts cyber-attacks significantly better than existing systems by continuously incorporating input from human experts (AI2 refers to merging AI with "analyst intuition": rules created by living experts). The team showed that AI2 can detect 85 percent of attacks --about three times better than previous benchmarks -- while also reducing the number of false positives by a factor of 5. The system was tested on 3.6 billion pieces of data known as "log lines," which were generated by millions of users over a period of three months. To predict attacks, AI2 combs through data and detects suspicious activity by clustering the data into meaningful patterns using unsupervised (automatic, no human help) machine learning.

Bots (or chatbots) seem to be taking the technology world by storm. Not a week goes by without some exciting announcements related to chatbots. Recently, at the F8 conference, Facebook announced a new chatbot platform for its messenger app. This announcement comes just a few days after Microsoft's release of its Bot Framework at the Build Conference. While we are still in the early days of the "Bot revolution," we can clearly see how chatbots are going to become an integral part of the consumer and enterprise software ecosystem.