An interesting conference last evening in Geneva on applied AI, which confirmed my perception that we are no longer in the times of spectacular news, like winning different games against human players, but that we are talking now of challenges of practical implementations. A couple of insights:
Category: Digital and AI
Think 2019
February 12 – 15 was the week for Think 2019, according to some analysis the most important tech event of the year. IBM assembled together all major individual exhibitions and this was the second edition of Think, now the main annual IBM conference. This time the event took place in San Francisco. I attended the event as one of the speakers on the subject of Watson Machine Learning and Watson Studio, and in my session I also had as guest Anyline, AI company from Austria.
The conference was successful and it was huge. Not only based on the number of participants (around 30’000), or sessions (more than 3’000), but also on the quality of discussions and topics. IBM events are not so much about announcements, but I will list and describe the most important ones in this post, along with highlights of the most recent and interesting technologies. Continue reading “Think 2019”
AI Conference in Serbia
“Serbian AI Valley”
On November 9 this year, I gave a speech at the first international AI conference in Serbia. The venue took place in the beautiful city of Novi Sad, in participation of around 300 AI developers and enthusiasts, data scientists, businessmen and representatives of academia. A first thing that you realize when going to Eastern Europe for a tech conference, is a high level of technical interests and expertise of the local people. This was the case also in Serbia. When you talk to them, you realize they are serious developers. It is estimated that there are around 50’000 people in total working in different IT jobs in Serbia, and this region around Novi Sad has actually the highest concentration of skilled IT professionals. Continue reading “AI Conference in Serbia”
Introduction to Deep Learning
As I have done this presentation already several times and got a good feedback, I’m posting here the slides and some useful links that allowed me to learn and understand DL better.
And some of the great resources I’d like to share
Continue reading “Introduction to Deep Learning”
Deep Learning and Watson Studio
It was fun to play with Watson Studio and create my own convolutional neural network. Watson Studio is complex but easy and graphically intuitive tool, and integrates well with Watson Machine Learning engine. I used standard MNIST data set (with images of hand-written digits), created three layers deep CNN, exported the algorithm in TensorFlow / Keras, trained the model both using GPUs on Watson and locally on my Jupyter Python Macbook environment and got nice results. To data scientists and AI developers : go and use it. And for all other enthusiasts, the best way to learn AI is to create it yourself 🙂 ! Continue reading “Deep Learning and Watson Studio”
From Deep Blue to AlphaZero
Twenty years ago, IBM impressed the world with its specialized chess-playing system that won the match against the best chess player of all times, Garry Kasparov. Last year, Google repeated the same achievement with its Go-winning solutions AlphaGo, AlphaGo Zero and most recently AlphaZero. How did this evolution happen and what are the key success factors of these remarkable events in the history of AI ?
Deep Blue
First, a little bit of history and background theory. The history of chess-playing AI solution goes back to 1957 when IBM research scientist Alex Bernstein wrote the first complete chess-playing program and ran it on IBM 704. But it was only 40 years later, in 1997 that computer-based solution reached such a level to surpass the best human performance. AI-based solutions for game-playing haven’t changed a lot during this period. The best practice was the use of opening books for the first dozen moves, and then use of Minimax algorithm for the search. Chess has up to 35 different moves, and can go to 50 moves deep, which makes it computationally impossible for any computer to identify all possibilities until the end of the game (35^50 is higher than the number of atoms in the universe). Due to the wide search, even nowadays this algorithm would go to the depth of maximum 10 levels within the allotted time for the move decision. Alpha-Beta pruning was developed to discard those subtrees that don’t contribute to the evaluation of the best moves for the Minimax algorithm. This helped to reach deeper levels (up to 18 levels from the current position). Once this depth is reached, an evaluation function must be defined to assess the strength of achieved positions and to grade the selected moves. Evaluation functions are very complex, and this is were humans typically excel. Garry Kasparov had for example extraordinary sophisticated evaluation capabilities, better than any other player. Continue reading “From Deep Blue to AlphaZero”
OpenStack 2016
When I went to my first OpenStack summit in Paris 2014, it was a huge thing. A vision of an open-source private cloud went out of the bottle and the hype exploded. But in these two years, in the market where I work, I haven’t seen too much adoption. So this year I went to the summit in Barcelona with low to moderate expectations. Luckily I’ve been wrong, because OpenStack is still a huge thing. And here below are just few reasons why.
First of all – numerous improvements, which make OpenStack more robust, easier to maintain and upgrade. Auto-remediation, for example will automatically add more hypervisors or evacuate VMs in case of HW failure, resolve rabbitmq problems, clean log files etc. With Newton release you will be able to upgrade the cloud without taking it down. Another interesting feature is that now you can create pools of external IP addresses, or create a compute node without an IP address for later addition.
Next, a complete set of new projects. For example Murano, which facilitates application deployment. Developers can package and publish their applications in a catalog, and deploy them with a push of a button. Or Sahara for automatic deployments of Hadoop clusters for big data analytics.
Accelerate Your Business with IBM Softlayer Cloud
I had an opportunity recently to learn a lot about Softlayer and get certified in Softlayer Solution Design, so I would like here to share my insights about some features, which may come as very useful for the post-digital world.
First of all I need to say that I work at IBM, and Softlayer is an IBM company. Softlayer cloud offering is actually, together with cognitive computing and Watson division, one of the strategic imperatives of the Big Blue. But I’m not directly attached to the Cloud Unit, so this certification was a challenge. IBM is known for the rigor in professional certifications. To get certified, it takes a lot of learning, practical exercises and experience, considerable time to prepare your certification package, and several levels of technical and peer reviews. All this hard work and learning has to fit into your regular work schedule, so in the end being certified at IBM is something IBMers are usually very proud of.
Continue reading “Accelerate Your Business with IBM Softlayer Cloud”
Natural Language Processing and Watson APIs
Cognitive APIs
Year 2016, marked by the win of Google DeepMind in strategy game Go is definitely the year of artificial intelligence. Cognitive computing, machine learning, neural networks, natural language processing – these new concepts offer tremendous opportunities for bringing god-like intelligence and judgment capabilities to our everyday technical and business applications.
In January we, at IBM, made a presentation to the FinTech community in Geneva on cognitive computing and on how to use Watson APIs. Here in this article is a summary of what cognitive computing is and how to put it easily in practice.
But first of all, I feel I need to clarify some concepts:
Continue reading “Natural Language Processing and Watson APIs”
Digital Lean
The Future of Project Management in the Digital Age
I am tempted to conclude that project management is on the decline. There is saturation in the number of project managers, in the activities called projects, in the number of projects managed by professional project managers, and many other metrics used to measure the effectiveness of project management discipline. It seems that this practice , as designed and used more than 15 years ago needs to redefine itself to be able to produce strategic advantage in the era of digital transformation.
If it is so, what other methods and concepts should business companies use to execute the work in an efficient manner? And also, what should the project managers do?
To answer these questions, we need first to remember how project management achieved such a high visibility and popularity in the last 15 years. Recall that this was the time of big investments in IT infrastructure and in-house developped IT applications. These investments required very strict scope management, time scheduling, resource deployment and cost management skills to make sure the company resources are used wisely.
But nowadays, the data is moving onto the cloud platform, and business value is created by using readily available APIs from an external ecosystem to make our business and application experiments. And it seems we don’t need project managers for that.
Let me then outline some advice on how to navigate through this new digital world without traditional project management.
Continue reading “Digital Lean”