Modeling & AI - Deepstash

Modeling & AI

Mathematical modelling consists of 3 components:

  1. Assumptions: These are taken from our experience and intuition to be the basis of our thinking about a problem.
  2. Model: This is the representation of our assumptions in a way that we can reason (i.e. as an equation or a simulation).
  3. Data: This is what we measure and understand about the real world.

Current AI is strong on the model (step 2): the neural network model of pictures & words. But this is just one model of many, possibly infinite many, alternatives. It is one way of looking at the world.

In emphasising the model researchers have a strong implicit assumption: that their model doesn’t need assumptions.But all models do.

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Although AI researchers can train systems to win at Space Invaders, it couldn’t play games like Montezuma Revenge where rewards could only be collected after completing a series of actions (for example, climb down ladder, get down rope, get down another ladder, jump over skull and climb up a third ladder).

For these types of games, the algorithms can’t learn because they require an understanding of the concept of ladders, ropes and keys. Something us humans have built in to our cognitive model of the world & that can’t be learnt by the reinforcement learning approach of DeepMind.

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Neutrality in AI

True neutrality in language and image data is impossible.

If our text and image libraries are formed by and document sexism, systemic racism and violence, how can we expect to find neutrality in this data? We can’t.

If we use models that learn from Reddit with no assumed model, then our assumptions will come from Reddit.

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Artificial Intelligence

What is Artificial Intelligence? Who knows. It’ s an ever-moving target to define what is or isn’t AI. So, I’d like to dive into a science that’s a little more concrete — Computational Intelligence (CI). CI is a three-branched set of theories along with their design and applications. They are more mathematically rigorous and can separate you from the pack by adding to your Data Science toolbox. You may be familiar with these branches — — Neural Networks , Evolutionary Computation , and Fuzzy Systems . Diving into CI, we can talk about sophisticated algorithms that solve more complex problem

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Building ethical AI

Companies are leveraging data and artificial intelligence to create scalable solutions — but they’re also scaling their reputational, regulatory, and legal risks. For instance, Los Angeles is suing IBM for allegedly misappropriating data it collected with its ubiquitous weather app. Optum is being investigated by regulators for creating an algorithm that allegedly recommended that doctors and nurses pay more attention to white patients than to sicker black patients. Goldman Sachs is being investigated by regulators for using an AI algorithm that allegedly discriminated against women by granting larger credit limits to men than women on their Apple cards. Facebook infamously granted Cambridge Analytica, a political firm, access to the personal data of more than 50 million users.

Just a few years ago discussions of “data ethics” and “AI ethics” were reserved for nonprofit organizations and academics. Today the biggest tech companies in the world — Microsoft, Facebook, Twitter, Google, and more — are putting together fast-growing teams to tackle the ethical problems that arise from the widespread collection, analysis, and use of massive troves of data, particularly when that data is used to train machine learning models, aka AI.

AI and Equality
  • Designing systems that are fair for all.

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AI adoption

In 2019, near 87% of data science projects did not get into production. However, due to COVID -19, most companies have scaled up their AI adoption and increased their AI investment.

In 2020, almost 50 % of enterprises employed an ML model. But to completely harness the power of AI, multiple models need to be created and deployed.

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