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) …
A curriculum is an efficient tool for humans to progressively learn from simple concepts to hard problems. It breaks down complex knowledge by providing a sequence of learning steps of increasing difficulty. In this post, we will examine how the idea of curriculum can help reinforcement learning models learn to solve complicated tasks. It sounds like an impossible task if we want to teach integral or derivative to a 3-year-old who does not even know basic arithmetics. That's why education is important, as it provides a systematic way to break down complex knowledge and a nice curriculum for teaching concepts from simple to hard. A curriculum makes learning difficult things easier and approachable for us humans.
Chatfuel provides flexibility in how you manage your bot's responses to free-form messages from users. One option is to automate these responses using the keywords rules built right in to your dashboard. Or, you can use JSON chat API to connect Dialogflow AI (artificial intelligence) to your bot. Dialogflow is conversational AI from Google used by some of the largest brands in the world. It allows your bot to "understand" and learn from the messages it receives, extract actionable data from those messages, and deliver even more accurate responses.
Tackling a machine learning problem might feel overwhelming at first. What model to choose?, which architecture might work best? In a process that is mostly driven by trial and error experimentation, those decisions result incredibly important. One aspect that really helps to navigate that universe of decisions is to clearly understand the nature of the problem. In machine learning scenarios, an important part of understanding the problem is based on understanding its environment.
At the present scenario, video games portray a crucial role when it comes to AI and ML model development and evaluation. This methodology has been around the corner for a few decades now. The custom-built Nimrod digital computer by Ferranti introduced in 1951 is the first known example of AI in gaming that used the game nim and was used to demonstrate its mathematical capabilities. Currently, the gaming environments have been actively utilised for benchmarking AI agents due to their efficiency in the results. In one of our articles, we discussed how Japanese researchers used Mega Man 2 game to assess AI agents.
In a joint research effort forged in 2017, the MIT-IBM Watson AI Lab has put significant resources into a new approach to AI that could provide CX and digital transformation specialists with more accurate intent recognition. Known as "neuro-symbolic artificial intelligence," this approach could allow companies to do more with less data and provide for greater transparency and privacy. Employing the approach to Conversational AI could give brands the ability to "add common sense" to their chatbots, intelligent virtual agents and to the prompts provided to live agents. The science combines the probabilistic pattern recognition capabilities of today's Deep Neural Networks (DNNs) and "deep understanding" with an approach to AI that is based on representations of problems, logic and search that are considered more "human-readable." In a new report, Dan Miller, lead analyst and founder with Opus Research, presents the possibility for enterprises to improve automated conversational systems with significant implications for customer care, digital commerce and employee productivity.
The folks at CallMiner had theories about what their speech analytics software would turn up when they launched an informal coronavirus customer research program back in March. The COVID-19 pandemic has upended life as we know it, and large corporations with big contact center operations have been forced to quickly adapt to the new work-from-home model. But when the AI work was done, there was one discovery that stood out above the others. One of the CallMiner customers that participated in Coronavirus Customer Thinktank is in the business of managing the medical waste streams from hospitals and other medical establishments. A pandemic is probably a big deal for medical waste company, figured Steve Chirokas, CallMiner's director of product and channel marketing.
Generation after generation, humans have adapted to become more fit with our surroundings. We started off as primates living in a world of eat or be eaten. Eventually we evolved into who we are today, reflecting modern society. Through the process of evolution we become smarter. We are able to work better with our environment and accomplish what we need to.
From Star Trek's Data and 2001's HAL to Columbus Day's Skippy the Magnificent, pop culture is chock full of fully conscious AI who, in many cases, are more human than the humans they serve alongside. But is all that self-actualization really necessary for these synthetic life forms to carry out their essential duties? In his new book, How to Grow a Robot: Developing Human-Friendly, Social AI, author Mark H. Lee examines the social shortcomings of the today's AI and delves into the promises and potential pitfalls surrounding deep learning techniques, currently believed to be our most effective tool at building robots capable of doing more than a handful of specialized tasks. In the excerpt below, Lee argues that the robots of tomorrow don't necessarily need -- nor should they particularly seek out -- the feelings and experiences that make up the human condition. Although I argue for self-awareness, I do not believe that we need to worry about consciousness.
When Pac-Man hit arcades on May 22nd 1980, it held the record for time spent in development having taken a whopping 17 months to design, code and complete. Now, 40 years later to the day, NVIDIA needed just four days to train its new GameGAN AI to wholly recreate it based only on watching another AI play through. Dubbed GameGAN, it's a generative adversarial network (hence, GAN) similar to those used to generate (and detect) photo-realistic images of people that do not exist. The generator is trained on a large sample dataset and then instructed to generate an image based on what it saw. The discriminator then compares the generated image to the sample dataset to determine how close the two resemble one another.
The virtual International Conference on Learning Representations (ICLR) was held on 26-30 April and included eight keynote talks. Courtesy of the conference organisers you can watch the talks in full and see the question and answer sessions. The aim of Mihaela's research is to contribute to the transformation of healthcare by rigorous formulation and development of diverse new tools in machine learning and AI. Her group has worked on many problems in medicine and healthcare, including risk prognosis, modelling disease trajectories, adaptive clinical trials, individualised treatment, early-warning systems in hospitals, and personalised screening. They needed to develop a variety of machine learning methods to carry out this work.