As I gaze into the palantir afforded me as a co-host of "The EdTech Situation Room" each week with Jason Neiffer (@techsavvyteach), this is part of the future I see for our students, our society, and ourselves in the coming decades. This 98 second video, which I titled "EdTech Situation Room Promo Trailer," is the result of my thinking about this question tonight. This question of what an emerging "artificial intelligence first" rather than "mobile first" worldview (which Google announced at Google IO 2017) should mean for schools is something I discussed on The EdTech Situation Room back on May 17, 2017, with Jason Neiffer (@techsavvyteach) and Ben Wilkoff (@bhwilkoff). Check out the "Narrated Slideshow – Screencast" and "Digital Storytelling" pages of ShowWithMedia.com for additional resources and examples related to these media project types.
Eric Sondheimer has been covering high school sports for the Los Angeles Times since 1997 and in Southern California since 1976. It's the hottest new toy in high school football: drones flying above practice fields filming practices. It's only a matter of time before someone has a drone football manager competition to see who can be the next F-16 pilot using remote control. At Loyola High, junior football manager Gabriel Danaj received a $1,500 drone from his grandmother as a gift and offered to use it to film Loyola practices.
Machine learning is, as you would likely imagine, extremely complicated, and not something your run-of-the mill computer engineer is going to be capable of without proper training. It requires someone with a background in computer science, likely with a doctorate in the sciences, as well as a significant amount of practical experience working with data at scale. Given that there is already a dearth of qualified data scientists, there is little to suggest that the situation is going to be any different when it comes to machine learning. Even The US Government has expressed concerns about the lack of AI talent.
Of these job postings, 61% in the AI industry were for machine learning engineers, 10% were for data scientists and just 3% were for software developers. According to a survey from Tech Pro Research, just 28% of companies have some experience with AI or machine learning, and more than 40% said their enterprise IT personnel don't have the skills required to implement and support AI and machine learning. Given that there is already a dearth of qualified data scientists, there is little to suggest that the situation is going to be any different when it comes to machine learning. Most companies undertaking machine learning projects already own and store vast quantities of data, but few enterprises have such copious quantities of data, and even then it is often siloed and requires aggregating, which is a lengthy and difficult process that few are resourced for.
Read the full ABC article and watch the video interview to learn more about Tanmay and his work in the field of AI. The Australian Broadcasting Corporation (ABC) recently profiled 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at age five, launched his first app at age nine, and has been working with IBM's AI and cognitive APIs for a couple of years now. ABC notes: "the Canadian teen has become a global force in programming and commands more than 20,000 subscribers on his YouTube channel that teaches computer coding." You can also watch Tanmay's video, "IBM Watson, Machine Learning: How to use the "Retrieve and Rank" service in IBM Bluemix", one of 80 tutorials he has created and made available on the "Tanmay Teaches" YouTube channel.
Data scientists have a variety of different skills that they bring to bear on Big Data projects. While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning. In today's post, I wanted to explore how machine learning skill proficiency varied across different types of data professionals. Specifically, we asked them to indicate their level of proficiency across 25 different data skills (including machine learning), satisfaction with work outcomes of analytics projects and their job role.
Data Scientists use Machine Learning (ML) skills to develop powerful algorithms to make sense of the avalanche of data. Moreover, when some of these Data Scientists plan on specializing in Machine Learning science or engineering, they struggle even further to adapt and hone their generic knowledge into the more specialized areas of ML. On the other hand, a bright computer scientist or engineer with some exposure to Machine Learning can surely hope to pursue a career in ML Engineering. The ML competition sites provide an excellent opportunity to bright Data Scientists to describe, present, and solve a particular problem through ML techniques.
Artificial intelligence and machine learning are ushering in the rise of smart machines that will be able to carry out many of the complex cognitive tasks that once seemed exclusive to middle class work in the knowledge economy. Already, doctors are using deeper learning to help diagnose illnesses, entry-level lawyers are finding themselves out-analyzed by machines that can harvest case history faster than any human, artificial intelligence is writing news stories and robots are staffing restaurants. This is just the beginning: one Oxford University study suggests that as many as 47% of current middle-class American jobs could get displaced or change significantly over the next two decades due to automation.
It is nearly impossible to meet all students' needs in a classroom with 22 plus students who have specific individual requirements. In any given classroom a teacher will encounter students with dyslexia, autism, ADHD, dyspraxia, auditory learners, visual learners, English as a Second Language learners, and the list goes on. Artificial Intelligence has the capability to personalize for each students' learning style which will result in students reaching their full potential. Artificial Intelligence will have the capabilities of delivering the information and knowledge to each student to fit their learning style.
At the end of the course – I have better handle on R, implementing ML algorithms using R, also have good confidence on competing in Kaggle with other ML practitioners. Moreover – I have gained confidence in learning & implementing any new ML technique as well in short timeframe. Further down, I'm planning to spend some more time on Feature engineering, getting handle on multiple ML techniques (like GBM, H2O, Adaboost, Gini scoring, Vowpal Techniques etc), increase competency with R, Research on Python libraries, be active on Kaggle platform, research on stacking/blending techniques. Also another puzzle to solve is to enable seamless integration of ML component (model which you developed) with your existing or new application architecture and enabling continuous model build process in your application architecture for the all sets of data.