If you're serious about big data and machine learning, you're already taking advantage of GPU, MIC, and FPGA powered analytics tools. This new breed of software can allow a single workstation to outperform a 100-node compute cluster in tasks like machine learning, graph analysis, financial modeling, and many other scenarios.
The following steps in the process was identified:
- Identify suitable class(es) of algorithms to move forward with.
- Train and validate multiple models.
- Consider tools for automated Machine Learning.
- Can Deep Learning give an even better result?
(Should you make calls to TensorFlow from your flow?)
HOW DO YOU MINIMIZE THE RISK AND MAXIMIZE THE BENEFITS WHEN ADOPTING DATA SCIENCE IN YOUR OPERATIONS?So, what to do?
AT FIRST THE SOLUTIONS ARE MINIMUM VIABLE PRODUCTS BUT AFTER SEVERAL ITERATIONS THEY SHOULD BE GENERATING VALUABLE AND ACTIONABLE INSIGHTS.The first solution might not even be an ML solution. If a gives value to the customer, then thats fine.
BUILD - MEASURE - LEARN. THEN DO IT AGAIN.A great motivational talk. It is indeed all about getting started.
Every phenomenon is the result of some process; contextual analysis of that phenomenon tries to understand the complexity of that process (the determinants and their dynamics) to better predict the outcomes.Trying to come up with good models to describe phenomenons is clearly difficult, but why is this?
From Artificial Intelligence in the 1950s to 1980s.Where Deep Learning is beginning to flourish.
Followed by Machine Learning in the 1980s until now.
And moving forward with Deep Learning, from the 2010s and onwards.
The business plan of the next 10.000 start-ups are easy to forecast...Indeed, many businesses, such as Google, are now transitioning from a Mobile First to an AI first business plan.
Take X and add AI...
- Lack of labelled data.Indeed, what are the full consequences for business models, organizations, and cultures, as we towards an AI first world?
- (Problems with) Model transparency and troubleshooting
(If you have a model with 2000 co-dependent variables how whould that be explained to humans?).
- Massive software engineering overhead.
- Lack of knowledge/experienced talent.
- Privacy and safety issues.
Key skills: Passion, focus, engagement, risk taking, value creators, in-depth industry experience, have a track record of failure...!And then of course, whether we were ''Troopers'' (The majority of people in a company),
Mobility, Virtual Reality, Augmented Reality, Bots, Blockchain, IOT, Cloud, Big Data, Machine Learning and AI- Are, of course, very exciting, and the starting place for businesses for years to come. But even these things will, of course, eventually give way to new ideas. And being positioned for that is what ''the game'' is all about.
Most projects are boring, because we have done them before.Actually, innovation and risk is not an option anymore in todays world.
- Promote curiosity.
- Solve real problems.
- Unlearn and learn.
- Identify opportunities to use ML.
- The ability to execute on those opportunities.
- Have a competitive advantage (higher quality ML solutions than your competitors).
- Broadband experience everywhere.With speeds up to 5-10 GBit/s, 5G will be about 100 times as fast as the current 4G network.
- Smart vehicles, transport and infrastructure.
- Critical control of remote devices. Robots.
- Interactions. Humans - IOT.
''We have for the first time an economy based on a key resource (information) that is not only renewable, but self-generating. Running out of it is not a problem, but drowning in it is.''A key question is of course how to work with these tidal waves of data out there.
- When interacting with the environment we get feedback sparsely.Still, in the real world we want solutions that can operate in:
- When interacting with the environment we get delayed feedback.
- Sparse reward environments.Among other interesting ideas, Merentitis (also) talked about the introduction of rewards (early on) that are less sparse and more smooth early on (Idea: Maximize reward while learning). Sounded very promising.
- Can deal with partially observable environments.
- Complex models. Indeed, they like models to be simple and understandable.Things get even worse, when the models aren't based on concrete physical things out there in the environment, or, worse still, try to predict chaos.
- Black box models, that we can't look into.
- What is the impact of the idea?
- Ease (How easy is it to implement the idea).
- Confidence (How confident are we that the idea will work).
Enactive Cognition Conference (Reading 2012) | Nasslli 2012 | WCE 2013 | Aspects Of NeuroScience 2017
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