Tag Archives: machine learning

Setting Up TensorFlow and Jupyter (for the Brisbane.ai Tutorials)

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!!

So here are the steps required (on Mac):

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A survival guide for the coming AI revolution (The Conversation)

Note: This is an article Natalie Rens and I wrote for The Conversation. Enjoy!


If the popular media are to be believed, artificial intelligence (AI) is coming to steal your job and threaten life as we know it. If we do not prepare now, we may face a future where AI runs free and dominates humans in society.

The AI revolution is indeed underway. To ensure you are prepared to make it through the times ahead, we’ve created a handy survivalguide for you.

Step 1: Recognising AI

The first step in every conflict is knowing your target. It is crucial to acknowledge that AI is not in the future; it is already here.

You are most likely using it on a daily basis. AI is the magic glue behind the ranking of your Facebook timeline, how Netflix knows what to suggest you watch next, and how Google predicts where you are headed when you jump in your car.

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.

In computer vision, features are the parts of an image that are used to classify an object. For example, the nose, ears and tail may be used as features to distinguish that a picture is a cat. Modified from pixabay.com

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.

Andrew Ng explains the major trends in AI and the impact it will have on business and society in the future.

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 fact that common AI relies on pattern recognition means that you can sabotage the way it processes data quite easily. But pose too much of a threat and Arnold Schwarzenegger may go back to try and kill you as an infant.

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.

Companies are already using AI to aid clinicians in medical diagnosis, personalise customer experiences and create agricultural methods that reduce the cost to the environment.

Some are even developing this relationship one step further with integrated systems that merge the human brain with AI.

Be ready to upskill where possible. AI can learn very well but it cannot learn flexibly (yet). You can. There are new jobs now available that did not exist five years ago.

If you allow AI to do the grit work, this can create opportunity to embrace the attributes that humans excel at, namely creativity, social intelligence and manipulation.

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.

 

About The Conversation:

The Conversation is an independent, not-for-profit media outlet that uses content sourced from the academic and research community.

 

Proposal, Proposal, Proposal

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 SNF Doc.Mobility proposal and started to look into writing my PhD proposal (2nd year review). *pfff*

Apart from that we are also involved in FP7-ICT (robotics) calls for machine Continue reading

“Toward Intelligent Humanoids”

We, the robotics group at IDSIA, are proud to announce the release of our new video Toward Intelligent Humanoids (aka The Story of Several Nerds and Their Adorable Baby Robot), which we invite you to view at our new IDSIA Robotics website: http://robotics.idsia.ch/im-clever/ (embed after the jump) Continue reading

Vision-eering

iCubRecently 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.
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Summer School 2011

Also this year I had the chance to attend a summer school, after ISRIS in 2009 and 2010 school at JAIST, I was going to this year’s Hands On Summer School on Neural Dynamics Approaches to Cognitive Robotics in Guimaraes, Portugal.

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).

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Project Work

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).

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