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.

“Applied AI/ML in the Workplace – Geek Food for Thought” – UT Austin presentation

Last Thursday we presented “Applied AI/ML in the Workplace –
Geek Food for Thought” at the University of Texas in Austin Computer Science department. Thomson Reuters is one of the Friends of the University of Texas at Austin that gives students an excellent opportunity to engage with the industry and learn more about companies that offers internships or job opportunities.

The speakers were me and Katherine Li, data scientist in my team. Special thanks to our co-workers and UT Austin alumni, Cameron Humphries, Director of software engineering, and Matthew Hudson, software engineer at Thomson Reuters. Also special thank you to Jennifer Green, senior talent acquisition partner in Thomson Reuters HR, and Ana Lozano, events program coordinator at UT Austin who helped set up the talk. More importantly, thank you UT Austin students for attending the event knowing that we missed more students because of conflicting class schedule and mid term exams.

I first talked about Thomson Reuters the company with a 100-year history, a global company, and its top-notch technology and careers development programs. I ran a video of our CEO and president, Jim Smith, explaining what makes Thomson Reuters Thomson Reuters. I then highlighted who are founding fathers of Thomson Reuters, beginning with Paul Reuter who founded Reuters News in 1851 and Roy Herbert Thomson who founded the company in the 1930’s which later became known as Thomson Corporation. Both companies later merged in 2008 and became Thomson Reuters. I hope to have made my point to the young audience that the Thomson Reuters founding fathers, Paul Reuter, who pioneered telegraphy and news reporting starting with pigeon posts, and Roy Herbert Thomson, First Baron Thomson of Fleet, were both entrepreneurs at a similar age as them.

I then provided an overview of Thomson Reuters Labs and listed some of the key innovative products including the latest WestLaw Edge, the most advanced legal research platform ever. I then moved to talk about AI and ran a video for our TR Labs CTO, Mona Vernon, speaking to The Economist early this year about AI and machine learning revolution. That was a great segue way to the main topic of the presentation, and that is applied AI in the workplace.

Through a couple of slides, I tried to make the point that students in the field of machine learning and artificial intelligence need to consider applying existing algorithms for their projects or their next start-up idea instead of building everything from scratch. It is quite understandable that students need to understand or even seek too contribute to the advancement of the core algorithms in the artificial intelligence. That is great and is very important but, unfortunately, it does not always lead to the next innovation or the next best product out there. The markets are hungry for applying artificial intelligence in the quickest time possible and in all the different ways that would have a societal impact. To illustrate the point, Katherine Li and I showcased four projects that leverage machine learning and natural language processing algorithms. We managed to get the applications working in a short time because we leveraged available cloud-based solutions notably Amazon AWS and Google Cloud and added our code for the projects. By spending less time building machine learning algorithms, we were able to focus more on the ideas and tie the different components into working prototypes.

You can check the presentation on this DropBox link (note: please download the deck and run the slides in presentation mode so that you can access the videos)

 

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Applied AI/ML in the Workplace – From Labs to Product, Real Use Cases at Thomson Reuters

Looking forward to presenting “Applied AI/ML in the Workplace – From Labs to Product, Real Use Cases at Thomson Reuters” at The University of Texas at Austin computer science department tomorrow. We will highlight some cool products that recently came out of the Thomson Reuters Labs and showcase how easy, beneficial, and cheaper nowadays for students to power their software with AI/ML algorithms. Check the event details

“AI & I at Work” – University of Texas at Dallas MIS Club Presentation

Update: Link to the presentation via DropBox  Note: you need to download the presentation and turn it into presentation mode to access the videos. Also, GitHub link for Sentiment-Tweets  and GitHub link for AutoML Course Reviews

I love my University of Texas in Dallas MIS Club audience! I started with running videos of key events in the history of data science and AI since the 60s. I never imagined that I would show Charlie Chaplin in the 80s IBM PC commercial or talk about the first desktop computer of the 60s, the Olivetti Programma 1, but I did :). I then talked on how the Python programming is a common instrument for both types of data scientists, the analytical and the AI product building. I promoted Jupyter Labs over Jupyter notebooks and encouraged the audience to leverage cloud notebooks using Google Collab. Then we went onto the cool stuff that I prepared for the event. I showed case my quick and easy implementation of building a Twitter sentiment analysis product with Python, Tweepy APIs, Django, Google Cloud NLP, and Docker containers. I then walked the audience in how I built a production-ready customers reviews rating model in less than 4 hours using Google AutoML for language processing and a Kaggle dataset that I found through Google new dataset search engine. After that, we had fun recognizing objects in the auditorium with Amazon DeepLens camera after I deployed a pretrained neural network model for object detection. Time went so fast when it is all love for computers. #loveoflearning

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