If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In a previous post we saw basic object recognition in images using Google's TensorFlow library from Smalltalk. This post will walk you step by step through the process of using a pre-trained model to detect objects in an image. It may also catch your attention that we are doing this from VASmalltalk rather than Python. Check out the previous post to see why I believe Smalltalk could be a great choice for doing Machine Learning. We provide a collection of detection models pre-trained on the COCO dataset, the Kitti dataset, the Open Images dataset, the AVA v2.1 dataset and the iNaturalist Species Detection Dataset.
The race to fully autonomous vehicles is on. In April, Elon Musk declared that Tesla should have over a million level 5 autonomous vehicles manufactured by 2020. To clarify, that means over a million cars equipped with the necessary hardware capable of driving with no help from a driver. In addition, government approvals will be necessary (read: mandatory) long before self-driving Teslas will be commonplace. In addition, Musk also sparked some lively debate when he commented that Tesla will not be relying on lidar, the laser sensor technology that self-driving cars from many other companies (most notably Google's Waymo) currently depend on for "seeing" lines on the road, pedestrians, and more.
Machine Learning (ML) is a type of Artificial Intelligence (AI) in which the main principle is that computers can learn and make decisions without relying on human programming. The technology is comprised of data consumption, algorithmic study, analytical model training, and building & predicting outcomes. ML has transformed the way most of the world's industries function, including financial, agriculture, real estate, science, manufacturing, shipping, healthcare, business, transportation, and commerce sectors. Machine Learning technology can be applied for various functions that developers may want to provide, including facial recognition, fraud detection, language identification & translation, contextual awareness, financial services, video analysis, autonomous robot, machine & car operations, conversation and speech patterns, text analysis, semantics, and even drawing. Application Programming Interfaces, or APIs are driving the insurgence of Machine Learning.
I see two main points of interest personally. The first is adversarial examples. There have been adversarially robust generative models developed, but it seems to me that there is more to be understood here. Obviously the'adversarial examples are features, not bugs' paper lays out a convincing argument around the theoretical meaning of the problem, but... is there some overarching pattern that can help distinguish useful features from brittle features? The main area I'm personally interested in though (nowhere near knowledgable enough to be caught up with current research, but it's what I'm working towards at the moment) is unsupervised model based reinforcement learning.
There is no better way to learn coding and AI than getting some hands-on practice. You can teach the robot to follow objects, avoid collisions, and a whole lot more with simple tutorials available. It is compatible with TensorFlow, PyTorch, Caffe, and MXNet frameworks. The kit includes a Leopard Imaging 145FOV wide angle camera, EDIMAX WiFi Adapter, SparkFun Micro OLED Breakout, and all the parts you need to get started.
Allied Market Research recently published a report, titled, "Artificial Intelligence Chip Market by Chip Type (GPU, ASIC, FPGA, CPU, and others), Application (Natural Language Processing (NLP), Robotic, Computer Vision, Network Security, and Others), Technology (System-on-Chip, System-in-Package, Multi-chip Module, and Others), Processing Type (Edge and Cloud), and Industry Vertical (Media & Advertising, BFSI, IT & Telecom, Retail, Healthcare, Automotive & Transportation, and Others): Global Opportunity Analysis and Industry Forecast, 2019-2025". According to the report, the global AI chip market was pegged at $6.64 billion in 2018 and is projected to attain $91.18 billion by 2025, registering a colossal CAGR of 45.2% during the forecast period. Rise in demand for smart homes & smart cities, surge in investments in AI startups, and advent of quantum computing have boosted the growth of the global AI chip market. However, dearth of skilled workforce hampers the market growth. On the contrary, rapid adoption of AI chips in the emerging countries and development of smart robots are expected to create numerous opportunities in the near future.
"Artificial intelligence is among the most consequential issues facing humanity, yet much of today's commentary has been less than intelligent: awe-struck, credulous, apocalyptic, uncomprehending. Gary Marcus and Ernest Davis, experts in human and machine intelligence, lucidly explain what today's AI can and cannot do, and point the way to systems that are less A and more I." --Steven Pinker, Johnstone Professor of Psychology, Harvard University, and the author of How the Mind Works and The Stuff of Thought "Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough. No matter how smart and useful our intelligent machines are today, they don't know what really matters. Rebooting AI dares to imagine machine minds that goes far beyond the closed systems of games and movie recommendations to become real partners in every aspect of our lives." Every CEO should read it, and everyone else at the company, too.