Often when we hear about artificial intelligence in legal it's addressed from a high-level, philosophical perspective that sometimes ignores the immediate use cases for practicing lawyers. Flying in the face of this, AI in legal took a major step forward on June 20 at the University of Chicago's Gleacher Center where leaders from law firms, legal technology providers, law schools and in-house legal departments gathered to examine AI's convergence within specific areas of legal, namely: e-discovery, contract review, contract analysis, litigation, and of course, legal research. There are roughly 900 legal tech startups in the legal ecosystem all attempting to improve how law is practice from solo shops to the biggest firms in the world. Among these tech providers, AI-based tools are becoming more widely accepted and better understood among lawyers. In an encouraging sign, more corporate clients are demanding their outside counsel use these technologies to be more accurate, more innovative and more efficient.

Garbage In/Garbage Out (GIGO) Many projects fail because project managers forget to check data quality or do not have the right approach to identify and resolve these issues. When we analyze incomplete or "dirty" data sets, our AI ends up making decisions and recommendations based on a poor foundation. Apples and Oranges Comparing unrelated data sets and/or data points will result in inferring relationships or similarities that do not exist. Overly Narrow Focus Some projects are designed to consider one data set without considering other data points that might be crucial for the analysis. For example, a project set up to analyze learner pass/fail rates while ignoring the course completion rate may inflate performance results. Cool but Useless Some AI projects are quick to deliver but fail to make a significant impact on the learner's everyday experience. Ensure that you have the right strategy to deliver the most value to your learners and avoid giving them something cool that doesn't really help them learn. My advice is to just get on with it. Make a point of learning something about AI and machine learning every day, always with an eye to how you might be able to use it in your own organization.

It may be the most exclusive school in the world. Housed somewhere inside the sprawling Hawthorne, Calif., headquarters of rocket manufacturer SpaceX, Ad Astra reportedly has less than 50 students between seven and 14-years old, perpetually-evolving curriculum, and no formal grading. Instead, Ars Technica reports, the mysterious not-for-profit school functions like a "venture capital incubator" in which students work in teams to drill into some of the most daunting topics of our time: robotics, nuclear politics and the dangers posed by artificial intelligence. The latter is no surprise considering that Ad Astra was founded by Elon Musk, the billionaire inventor who has been stridently warning about the risks posed by intelligent machines for several years now. "I just didn't see that the regular schools were doing the things that I thought should be done," Musk told a Chinese TV station in 2015.

More than a hundred startup teams are beginning a training and mentoring program this month at the Berkeley SkyDeck Accelerator--the largest group ever accepted to the UC Berkeley program since it was founded in 2012. Aside from the funding, free office rent, and other resources offered by the startup accelerator located near the edge of the Berkeley campus, the program is also setting an example for its young companies--as an organization going all out for growth. SkyDeck, originally launched as free office space where startups could roost and benefit from the advice of mentors from the university community, now partners with an affiliated venture capital firm, Berkeley SkyDeck Fund, which invests $100,000 in each company entering the program's formal six-month session as a "Cohort" member. That's comparable to the $120,000 invested in each startup nurtured by the influential Silicon Valley accelerator program Y Combinator. For the fall session, 22 Cohort startups were chosen, and an additional 80 teams were admitted as "HotDesk" members who can attend workshops, consult mentors, use office desks as available, and prepare for their company's next stage.

Machine learning packages take care of the building and implementing the top machine learning algorithms, creating workflows, and in general helping to solve machine learning problems. They provide the primary toolkit for different classification, regression, and other problems. As an integral part of data science, data manipulation and analysis field represents libraries that carry out data scraping, ingestion, cleaning, pre-processing and other operations that allow you to "play with the data" and as a result to perform the analysis itself. With the help of visualization packages, you can display the data visually which is necessary for better understanding and interpreting the data. These packages contain numerous visualization charts as well as different options for representation.

Engineering students at the University of Leicester (www.le.ac.uk) have developed a miniature autonomous?drive-by-wire? It is a 1:6 scale version of the LUTZ Pathfinder Pod developed by the Transport Systems Catapult (TSC).

IBM's Project Debater can have meaningful conversations with humans. As the adage goes, "to err is human." Mistakes and bias are built into the human condition. We can try our very best to maintain objectivity, but more often than not we allow personal biases to creep into our everyday decision making. While most of the time these biases are harmless, this can become a huge problem when your job is hiring people.

Two years ago, Sanjaya Wijeratne--a computer science PhD student at Wright State University--noticed something odd in his research. He was studying the communication of gang members on Twitter. Among the grandstanding about drugs and money, he found gang members repeatedly dropping the emoji in their tweets. Wijeratne had been working on separate research relating to word-sense disambiguation, a field of computational linguistics that looks at how words take on multiple meanings. The use of jumped out as a brand new problem.

A study by Stanford University indicates that virtual reality, adaptive learning or analytical learning will be common in the classroom within fifteen years. Although Artificial Intelligence (AI) is already part of our lives, it is still strange to hear about it in areas such as education, where the reality of the classroom advances at a much slower pace than that of technology. However, it is precisely the educational field that could be reinforced and transformed the most thanks to the new artificial intelligence systems and their capacity to contribute to the personalisation of learning. This is what a group of researchers and academics believe that, backed by Standford University, published last September the report Artificial Intelligence and Life in 2030. According to the study, virtual reality, adaptive learning, analytical learning and online teaching will be common in classrooms in just fifteen years.

"AI is the new electricity" said Andrew Ng, computer scientist and co-founder of online university, Coursera. Artificial intelligence (AI) has indeed come a long way from being confined in the research laboratories or science fiction movies. It is a persistent reality in today's world, which has permeated every layer of business, causing disruptions of unprecedented magnitude. In such a short span of time, AI has an incredible impact in reshaping the entire retail landscape by boosting productivity, increasing accuracy and improving reliability of business intelligence. AI and machine learning have already begun to make sweeping changes to the entire retail ecosystem.