To many, Artificial Intelligence (AI), is still in the realm of science fiction: decades away, and far removed from our current reality. However, increasingly, we are interfacing with platforms and systems powered by AI, such as smart watches and virtual assistants, such as Siri and Alexa. More importantly, in the coming decade, AI will drive and underpin a broad range of processes, and consequently change the ways we live and work. This episode is also available on SoundCloud, Apple iTunes, Google Play Music, Spotify, Amazon Music (NEW!) and on Stitcher! Over the past few months, there have been some new developments in the AI space that highlight the fact that AI is developing rapidly.

Those looking to add to their Google home setup can get the biggest Nest smart display for less today. Adorama has a one-day-only sale that knocks $60 off the Nest Hub Max, bringing it down to $169. That's even cheaper than we saw it a couple of weeks ago when a bunch of Nest gadgets were discounted across the web, and it's $11 less than the device's Black Friday price last year. The Nest Hub Max earned a score of 86 from us when it first came out in 2019 and it remains a good option for those that rely on the Google Assistant and want a larger home hub with advanced smart features. It has a spacious 10-inch HD touchscreen on which you can do things like take a Zoom call, watch YouTube or Netflix and control all of the smart lights, thermostats and other gadgets in your home.

The human activity of tagging content such as text, images, and videos, so that machine learning models can recognize them and use them to generate predictions, is known as data annotation. When we label data elements, ML models accurately understand what they are going to process and retain that information to automatically process the available information, based on existing knowledge, to make decisions. Data annotation refers to the process of attributing, tagging, or labeling data. To summarize, data labeling and data annotation are both concerned with labeling or tagging relevant information/metadata in a dataset so that machines can understand what they are. The dataset can take any form, such as an image, an audio file, video footage, or even text.

Artificial Intelligence has already moved into many facets of our daily lives from Siri to Cortana, Alexa to Google Duplex, in banks, in CCTV cameras on the street, Conversational AI, Emotional AI, flying drone swarms, Chatbots, language translators, facial recognition and Social media. We are all surrounded by variety of new Artificial Intelligence devices. We have become accustomed to sharing our reality with intelligence simulations. By means of smart algorithms, machines today are capable of doing incredible things with facial and speech recognition. With error rates of under five percent, many systems can perform better than humans.

China, which plans to be a world leader in AI by 2030, is predicted to deploy more ‘virtual’ employees this year.

What are you currently working on or worked on before? I worked on international research projects related to Artificial Intelligence research areas. My main research area is reinforcement learning. Apart from that, I engaged in machine learning-related research projects related to personalized recommendations, cancer chemotherapy treatments, frailty analysis, cancer patients' survival rates analysis, etc. Other core research areas I have worked in areas like the travel industry, Internet, Internet of Things, air pollution, behavioral sciences computing, convolutional neural nets, environmental factors, health care,human-computer interaction, recommender systems, recurrent neural nets, sentiment analysis, social networking (online), time series, unsupervised learning, etc. I am seeking research collaboration opportunities, academic positions, industrial AI events, worldwide, and would love to work on collaborative projects.

Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to each individual user's local data distribution. To deal with data heterogeneity, model personalization with on-device learning is a potential solution. However, on-device training using a user's small size of local samples will incur severe overfitting and undermine the model's generalization ability. In this work, we propose a new device-cloud collaborative learning framework, called CoDA, to break the dilemmas of purely cloud-based learning and on-device learning. The key principle of CoDA is to retrieve similar samples from the cloud's global pool to augment each user's local dataset to train the recommendation model. Specifically, after a coarse-grained sample matching on the cloud, a personalized sample classifier is further trained on each device for a fine-grained sample filtering, which can learn the boundary between the local data distribution and the outside data distribution. We also build an end-to-end pipeline to support the flows of data, model, computation, and control between the cloud and each device. We have deployed CoDA in a recommendation scenario of Mobile Taobao. Online A/B testing results show the remarkable performance improvement of CoDA over both cloud-based learning without model personalization and on-device training without data augmentation. Overhead testing on a real device demonstrates the computation, storage, and communication efficiency of the on-device tasks in CoDA.

Something a little more unusual in this blog post as we are going to be exploring how Artificial Intelligence can be a tool that we can use to help and support people, of any age with additional needs or disabilities, in our churches. "Technology has changed the world, bringing knowledge within reach and expanding a range of opportunities. Persons with disabilities can benefit enormously from such advances, yet too many lack access to these essential tools…" So, has anything changed since then? And what does today's Artificial Intelligence, or'AI', offer as technological solutions for disabled people, particularly in our church settings? What can we learn about'AI' together, that can enable us to better serve and support disabled people in our church communities?

What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today. I'm sure I'm not the only one who's accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that? Facebook: So powerful that world governments are worried that the newsfeed has too much influence on people!

If you're looking to light up your humble abode with smart bulbs, we've got the deal for you. Philips is selling its four-bulb E26 starter kit for just $60. The bulbs are the equivalent to 60 watt incandescent bulbs, but have a maximum operation power of 10 watts. The bulbs integrate with all the popular voice assistants including Amazon's Alexa, Apple HomeKit, and Google Assistant. You can also control the lights from your smartphone using the Hue app.