Lightning Talk – Course Sentiment Tweets with AutoML

I presented a 15-minute lightning talk on leveraging AutoML for sentiment analysis.

Quick and easy AutoML for Sentiment Analysis and Classification tasks

“Machine learning algorithms have evolved significantly in the last few years. AutoML is one of the latest advancements in the field that allows anyone to build and deploy AI products without requiring extensive knowledge in the field. The lightning talk will show case how one can build a production-quality sentiment analysis model using Google AutoML and Google Cloud with the least coding possible.”

I first showed case how to upload datasets directly into Google AutoML NLP portal and, from there, train a model and perform predictions. After that, I showed how I integrated the sentiment analysis model into analyzing Twitter stream using Django, Docker, Twitter API/Tweepy, Jupyter Notebooks, and PostGresQL, I published the code on GitHub under hoteit/courses-sentiment-tweets

Give or Take

There are two types of people. Those who give more than they take, and those who take more than they give. The ratio between give and take can vary from zero to infinity such as zero giving and all taking or no giving but all taking. I think that knowing where you stand in such a formula can make a huge difference in one’s own life accomplishments and everyday human interactions. Life nurture such as experience can dynamically and actively influence the change in ratio between giving and taking, while life nature, basically age and genetics, can yield an uncontrollable and more slower impact or hardly a change in behavior. In couple of weeks people will once again plan their new year resolutions. Do consider the giving versus taking ratio formula for the years to come. Hopefully it should be seriously more on the giving rather than the taking side, not 50/50!

AutoML – expert AI for the inexperts and businesslike

Nearly everyone around by now has either heard or used artificial intelligence (AI) and machine learning (ML) in some form or fashion. Some students are already publishing papers in the field while other students are applying various AI techniques in their research, internships, or just for fun. Professionals in the industry have either incorporated some form of AI/ML into their product or services or are currently considering it. Either way, AI and ML have a lot to offer but not without a good amount data, significant processing power, right skillsets, and a lot of patience with design and execution of such projects. For that problem, AutoML is a promising new technique in the field that allows researchers and professionals to make use of pre-trained models and cloud-based services to roll out AI solutions much more rapidly than building machine learning models from scratch. AutoML provides the methods and processes to apply, integrate, deploy, and scale machine learning intelligence without requiring expert knowledge. Major AI platforms, starting with Google and followed by Microsoft, H20.ai, and others are priming AutoML as the next evolutionary frontier in artificial intelligence so that humans can spend zero time recreating machine learning models from scratch, and, instead, focus on applying the models while letting machines take care of building them.

AI/ML supporting hometowns of international students: what, how and why?

Nearly everyone around by now has either heard or used artificial intelligence (AI) and machine learning (ML) in some form or fashion. Some students are already publishing papers in the field while other students are applying various AI techniques in their research, internships, or just for fun. Professionals in the industry have either incorporated some form of AI/ML into their product or services or are currently considering it. Either way, AI and ML have a lot to offer but not without a good amount data, significant processing power, right skillsets, and a lot of patience with design and execution of such projects. For that problem, AutoML is a promising new technique in the field that allows researchers and professionals to make use of pre-trained models and cloud-based services to roll out AI solutions much more rapidly than building machine learning models from scratch. AutoML provides the methods and processes to apply, integrate, deploy, and scale machine learning intelligence without requiring expert knowledge. Major AI platforms, starting with Google and followed by Microsoft, H20.ai, and others are priming AutoML as the next evolutionary frontier in artificial intelligence so that humans can spend zero time recreating machine learning models from scratch, and, instead, focus on applying the models while letting machines take care of building them.

References: -PwC 2017 report “PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution” https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html

-National Foundation for American Policy (October 2017) “The Importance of International Students to American Science and Engineering” http://nfap.com/wp-content/uploads/2017/10/The-Importance-of-International-Students.NFAP-Policy-Brief.October-20171.pdf

Chess and Coding

Even software professionals can be rated like chess players. Whether it is successfully delivering a complex project or getting rewarded based on an innovative solution, the pressures of keeping up with one’s own rating even if was something virtual can lead to self-imposed stress and fear. When a chess player is facing tough competition in a rated game, the fear of loosing that leads to a decline in rating can be overwhelming. But if the game is treated as unrated then ones’ rate would not be impacted by the win or loss. Stakes will be lower and the pressure would be less. This can apply to software developers as well. Occasionally pick up an unrated project, such as something of your own choosing or a new type of code you always want to try but were afraid to get measured by. Do it under your own pace. Once you feel more comfortable with it take a similar project as a rated challenge at your job or in your community. You will then feel more confident and more comfortable with what you do. That’s what I do when I either code or play chess. #technology

Staying on top of your game

The big challenge to stay on top of your game is to stay focused, be organized, and keep learning. When you are young, supervised learning is the way to go but it gets harder with unsupervised learning as you age. Hence, to keep us humans fit just as machine with deep learning are these days, I believe that reinforcement learning is the way to go for humans and machines alike so as to continue advancing together. Learn from mistakes, think of alternative paths, and stay positive on every track. (last minute thoughts before heading to the gym in the morning and later to work 🙂 ) #machinelearning #humanlearning

Are you a scuba or a skin diver

Both scuba divers, those with oxygen bottles under water, and skin drivers, those floating over the water looking down with snorkeling masks, can find treasure. Which one can be like you? A scuba diver would stay deep under water but can’t stay long because the oxygen in their bottle is limited and their equipment is heavy over the water. The skin diver carries light equipment and can cover more surface area because they breath unlimited air but they can’t search deep down underwater like scuba divers. When it comes to research and learning opportunity in the workplace, which one can be you? The answer is in nature. Watch how the cormorants birds that dive deep under water does it with speed and focus then think of its approach as an opportunity for you to do just that in your next project https://lnkd.in/eNkYJkD 

#education #digitaltransformation #research #innovation

Game-playing in the enterprise

Scott Adams, creator of Dilbert and computer game software pioneer, once said “man is a game-playing animal, and a computer is another way to play games”. The breakthrough in personal computer also started with games thanks to the homebrew nerds that collaborated together in the late 70s. I think if we maintain a philosophy of what we do for our customers in the enterprise is with a game playing mindset where the winner must always be the customer and the goal is to ensure customers are first then it would be a win win for everyone – the customer, the business, and us.

Sum not necessarily better than its parts

The sum of all parts does not always make it better than its parts. Take the words “block” and “chain” in blockchain. Blocks in toys were invented as early as the 1500s way before Minecraft made history with its digital blocks. Chains were used in 225BC to draw buckets of water and, in 16th century, Leonardo Da Vinci sketched the first steel chain. Such inventions took ages to popularize, but blockchain is not an invention. It is a valuable functionality that, if we just treat it as such and not as some grandeur product, we can really invent products that make use of it. Let’s take a simple pragmatic approach and not force ideas for the sake of ideas to deploy blockchain. Da Vinci or Minecraft players will probably second that idea.