With the emergence of 5G technology, the global AI Market is likely to gain momentum, states Fortune Business Insights in their latest study. The study is titled, "Artificial Intelligence Market: Global Market Analysis, Insights, and Forecasts, 2018 – 2025." According to the study, the global AI market will exhibit remarkable growth owing to factors such as the advent of machine learning into various other industries such as education, health, and others. Technological advancements and the launch of smart devices are creating lucrative growth opportunities for the artificial intelligence market. The rising popularity of machine learning technology is likely to help this segment pick pace over the forecast period.
Understanding how my GPS works doesn't prevent it from degrading my map reading skills. Any skill you don't practice gets rusty. As Dunning-Kruger tells us, the more sure we are about our understanding the less understanding we are likely to have. AI that tells us what we already think to be true may well be simply crystallizing our biases in a non-disputable form. For example there was an article a few months back in Technology Review about using AI to (a) decide which convicted criminals to incarcerate based on an AI-generated "recidivism score", and (b) the use of AI to direct police to places where crime is most likely.
When we create our machine learning models, a common task that falls on us is how to tune them. People end up taking different manual approaches. Some of them work, and some don't, and a lot of time is spent in anticipation and running the code again and again. So that brings us to the quintessential question: Can we automate this process? A while back, I was working on an in-class competition from the "How to win a data science competition" Coursera course.
Machine-learning chatbot systems can be exploited to control what they say, according to boffins from Michigan State University and TAL AI Lab. "There exists a dark side of these models – due to the vulnerability of neural networks, a neural dialogue model can be manipulated by users to say what they want, which brings in concerns about the security of practical chatbot services," the researchers wrote in a paper (PDF) published on arXiv. They crafted a "Reverse Dialogue Generator" (RDG) to spit out a range of inputs that match up to a particular output. Text-based models normally work the other way, where outputs are generated after having been given an input. For example, given the sentence "Hi, how are you?", a computer learns to output a response like "Fine, thank you" as it learns that is one of the most common replies to that question in training data.
Each day, we read more news about artificial intelligence (AI), machine learning (ML) and their uses for not only work but, more importantly, education. About a year ago, I started to research these areas. While I understood the concepts of both and could offer a decent definition, I was not able to easily identify what it might look like in today's classrooms. My first interaction with machine learning came some years ago when I worked on my Spanish translation coursework. Our focus was on the level of accuracy that ML-translation provided for students and for businesses looking to use these services.
This is a 1-week/10 hours long, part-time and instructor-led training offered in evening time (New York Timezone) by 6FS.io, a San Francisco based technology company. This training program is built based on 6FS team's years of experience in building large-scale solutions using various various Big Data and AI/ML technologies. This is not a book-based training, rather a hands-on, interactive experience app building apps using AI/ML, delivered by experienced startup CTOs. While learning basic concepts like Python, Jupyter notebooks, and training models and human powered labeling, you'll also learn practical problems and solutions, based on how Dean and Adrian built technology stacks in their previous startups. Let's build a project to gather data from human labeling service like AWS Sage maker GroundTruth.
San Francisco is known as a hub of tech innovation, making USF an ideal place to study computer and data science. The location gives students the opportunity to connect professionally with companies everyone knows: Google, Twitter, Facebook – the list goes on. But what opportunities does USF offer students to participate in peer reviewed scholarship, a place where current students and faculty can connect over tech R&D on campus? As of Fall 2018, the answer comes in the form of the weekly MAGICS Lab meetings, a way to gain valuable mentorship and learn about emerging technologies, a place where undergraduate, graduate students, and faculty all have the opportunity to learn, research, and publish together. This group welcomes all skill-levels, from novice to seasoned researchers alike.
Link: The Complete Python 3 Course: Beginner to Advanced his course is designed to fully immerse you in the Python language, so it is great for both beginners and veteran programmers! This diploma in C and Python programming course is a great way to get started in programming. It covers the study of the C and Python group of languages used to build most of the world's object oriented systems. The course is for interested students with a good level of computer literacy who wish to acquire programming skills. It is also ideal for those who wish to move to a developer role or areas such as software engineering.