The easiest way (I found) to setup TensorFlow on your system is by using a Docker install. Its advantages are that it just works out of the box, all dependencies are there and you can start building deep models within minutes! Warning: it does not allow you to accelerate the learning on a GPU though!!
AI is not a new concept. It was born in the summer of 1956, when a group of pioneers came together with a dream to build machines as intelligent as humans. AI encompasses disciplines such as machine learning, which can find patterns in data and learn to predict phenomena, as well as computer vision, speech processing and robotics.
The main technique behind the current hype around deep learning is artificial neural networks. Inspired by models of the brain, these mathematical systems work by mapping inputs to a set of outputs based on features of the thing being examined. In computer vision, for example, a feature is a pattern of pixels that provides information about an object.
Most commonly, the supervised learning approach requires the computer to “learn” these associations by training on big data sets labelled by humans. What began with classifying cat videos has now extended to applications such as driving autonomous vehicles.
Step 2: Identify where AI thrives
With this knowledge, we can start to understand where AI is optimally positioned to take over. Have a look around you and take note of tasks that require huge amounts of data processing.
For example, no human would or could look through everyone’s click patterns on Google to figure out what someone wants.
Even the more advanced capabilities that AI has demonstrated in winning AlphaGo, video games and, most recently, poker rely on training on thousands and thousands of trials.
Essentially, AI is particularly good at any task that requires an enormous amount of repetitive processing. If this sounds like your job, it might be time to start thinking of a survival plan.
To evaluate your “automation risk”, type in your job on this site to find out what researchers have calculated for your field. Even if you’re not worried, have a look. The prepared person stays ahead.
Step 3: Devise an action plan
You now have two choices:
Option A: Resistance
Your first option is to fight back. This may be your natural reaction and, as in during the industrial revolution, you would not be alone in wanting to oppose the change.
The nature of the human race is that we will always strive towards the next advancement. Resisting change out of fear of its disadvantages may work in the short term but will only make you more likely to be left behind in the future.
Option B: Make friends with AI
The far superior strategy is to form a treaty. Accept that AI will increasingly become a part of society and look for possibilities to collaborate. There is a huge potential for AI to assist in places where humans fall short, precisely because of the processing power.
As with every big change, there are fears about new technology like AI. Ultimately, the way to survive the AI revolution is to embrace the partnership. Understand the potential that AI has to improve the world around you and look for those opportunities to implement positive change.
If you prepare yourself, you may find the AI revolution allows you not only to survive but to be an even better version of your human self.
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Recently we have been quite busy with writing, proof-reading and submitting various proposals. The IDSIA Robotics Lab is involved in some new interesting projects, one in the FP7-SPACE-2013 call. The GMV lead project aims to look at autonomous operations of a Mars rover with a focus on biological evidence/fact finding. In the meantime I was also writing an SNFDoc.Mobility proposal and started to look into writing my PhD proposal (2nd year review). *pfff*
Recently I have been working a lot on trying to make the iCub see things. A fully integrated computer vision or robotic vision system is a quite tricky mathematical and engineering problem. Here at IDSIA we were trying to develop an easy to use system that would allow to rapid prototyping (offline) vision modules for the iCub, mainly to detect and localise objects the robot is in later stages supposed to manipulate and interact with. Continue reading →
This school focused on the idea of Dynamic Neural Fields (similar but according to the presentations more powerful than Neural Network approaches) and how to use them in various robotic systems. The presentations, mainly given by researchers from the University of Bochum and Minho, ranged from computer science to neuroscience and included various applications, such as on mobile and humanoid robots (including the Nao and ARoS, a humanoid (upper body) robot built at Minho). The school ended with a project to be implemented (and yes there is a video of ARoS after the jump).
In the last few weeks I was getting started with looking into the projects I am (supposedly) working on during at least the next year here at IDSIA. For the foreseeable future I will focus my attention on mainly two projects, both funded by the European Union’s Framework Programme. The first project is called STIFF and is lead by the German Aerospace Center (DLR), more precisely their section working on biorobotics. The second one is named IM-CLeVeR and the partners include CNR (in Rome), Universities of Ulster and Aberstwyth (UK).