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Posts Tagged ‘crashcourse

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Ooh! That was my reaction to this announcement by CrashCourse that there will be a 14-part series on Zoology.

My first academic love was Biology and I have an Honours degree in Zoology. I used to study with fish hormones with radioactive isotopes, bash trails for species counts and ecological conservation, and take photos and videos for documentation. I even used to process dead animals at the Singapore Zoo!

Just thinking of what I used to do as an undergraduate (and shortly after) fills me with warm nostalgia. I look forward to the new CrashCourse series and have no doubt that it will trigger more good memories.

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This was the final episode of the the CrashCourse series on artificial intelligence (AI). It focused on the future of AI.

Instead of making firm predictions, the narrator opted to describe how far AI development has come and how much further it could go. He used self-driving cars as an example.

Five levels or milestones of self-driving AI.

Viewed this way, the development of AI is gauged on general milestones instead of specific states.

The narrator warned us that the AI of popular culture was still the work of science fiction as it had not reached the level of artificial general intelligence.

His conclusion was as expected: AI has lots of potential and risks. The fact that AI will likely evolve faster than the lay person’s understanding of it is a barrier to realising potential and mitigating risks.

Whether we develop AI or manage its risks, the narrator suggested some questions to ask when a company or government rolls out AI initiatives.

Questions about new AI initiatives.

I thoroughly enjoyed this 20-part series on AI. It provided important theoretical concepts that gave me more insights into the ideas that were mentioned in the new YouTube Original series, The Age of AI. Watching both series kept me informed and raised important questions for my next phase of learning.

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This was another episode that focused on hands-on Python coding using Google Colaboratory. It was an application of concepts covered so far on dealing with biased algorithms.

The takeaway for programmers and lay folk alike might be that there is no programme free from undesirable bias. We need to iterate on designs to reduce such bias.

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This was an episode that anyone could and should watch. It focused on bias and fairness as applied in artificial intelligence (AI).

The narrator took care to first distinguish between being biased and being discriminatory. We all have bias (e.g., because of our upbringing), but we should prevent discrimination. Since AI adopts our bias, we need to be more aware of ourselves so as to prevent AI from discriminating harmfully by gender, race, religion, etc.

What are some examples of applied bias? Google image search for “nurse” and you are likely to see photos of women; do the same for “programmer” and you are more likely to see men in the photos.

The narrator suggested five sources of bias. I paraphrase them as follows:

  1. Existing data are already biased (e.g., the photo example above)
  2. New training data is unbalanced (e.g., providing photos of faces largely from one main race)
  3. Data is reductionist and/or incomplete (e.g., creative writing is difficult to measure and simpler proxies like vocabulary are used instead)
  4. Positive feedback loops (e.g., past actions are repeated as future ones regardless of context)
  5. Manipulation by harmful agents (e.g., users teaching Microsoft’s Tay to tweet violence and racism)

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Finally. An episode on how search engines use AI to help (or not help) us find answers to questions.

The narrator likened search engines to library systems: They had to gather data, organise them, and find and present answers when needed.

The gathering of data is done by web crawlers — programmes that find and download web pages. The data is then organised by reverse indexes (like those at the back of textbooks).

The indexed web content is associated with numbers. Each time we search with an engine, these numbers are then linked to associated web content.

Example of indexing.

Since there is so much content, it needs to be ranked by accuracy, relevance, recency, etc. We help the AI to this with bounces (returning to the search) to click-throughs (staying with what we were presented).

The narrator also explained how we might be presented with immediate answers and not just links to possibly relevant web resources. AIs use knowledge bases instead of reverse indexes.

Knowledge bases might be built with NELL — Never Ending Language Learner. The video explains this better than I can.

NELL — Never Ending Language Learner.

Fair warning: Search engines still suck at questions that are rarely asked or are nuanced. AI is still limited by what data is available. This means that it is subject to the bias of people who provide data artefacts.

The next episode is about dealing with such bias. Now the series gets really interesting!

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This was an episode that would make a novice coder happy because it provided practice.

It did not apply to ame because I was merely getting some basics and keeping myself up to date for a course I facilitate.

In this episode, the host led a session on how to code for a movie recommendation system. To do this, he revisited concepts like pooling large datasets, getting personalised ratings, and implementing collaborative filtering. In doing so, this host suggested solutions for incomplete data, cold starts, and poor filtering.

The next episode promises to provide insights on how search engines make recommendations.

It took a while, but CrashCourse finally provided some insights into how YouTube, Netflix, and Amazon make recommendations.

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Long story short: The AI recommendations are based on supervised and unsupervised learning. The interesting details are that the algorithms may be content-based, social-based, or personalised.

Content-based algorithms examine what is in, say, YouTube videos. Social-based algorithms focus on what the audience does (e.g., likes, views, time spent watching). As we have different preferences, algorithms can learn what we like and serve us similar content or content from the same provider.

The recommendations we see on YouTube are a combination of all three and the process is called collaborative filtering. This relies on unsupervised learning to predict what we might like based on what other users similar to us also like/watch.

The AI might make mistakes in the recommendations. This can be due to sparse data (e.g., low views, low likes), cold starts (i.e., AI does not know enough about us initially), and statistics (i.e., what is likely is not the same as what is contextually relevant). A good example of this sort of mistake is online ads.

Some pragmatics: To get good recommendations, we might subscribe and like videos from content creators we appreciate. To avoid getting tracked, we might use the incognito mode in most modern web browsers.

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This week’s episode countered the mainstream and entertainment media message that artificial intelligence (AI) will take over all our jobs and eventually us as well. It focused on how humans and AI can collaborate and complement one another.

AI is quick, consistent, and tireless. But it is poor with insight, creativity, and nuance, traits that we possess despite ourselves. The narrator related an example of how chess players worked with AI to beat human chess masters or AI-only opponents.

Beyond chess, the narrator suggested that AI could help with medical diagnoses. It can focus on rote tasks and processing large amounts of information and combine its findings with a doctor’s experience and knowledge of a patient.

In engineering, AI could suggest basic designs of structures based on existing rules while humans might consider the practicality of those designs in context. In human development, AI could artificially give us more strength, endurance, or precision, e.g., robot exoskeletons, remote surgery.

As much as AI helps us, we also help AI. We provide data for AI every time we contribute of any online database. When AI spits results out based on algorithms, it often shows us the products but not the processes; humans can provide insights into those processes or fine-tune them.

AI has no moral value systems. That is a human thing. But so is bias, which happens to be the focus of the next episode.

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This week’s episode was a continuation was of last week’s. It was less about theory and more about testing some Python code and Google Colaboratory.

My goal of watching and taking notes on this series was basic knowledge building and not actual coding. But the episode gave me insights into the processes of this form of AI training.

I also found the transfer of biological concepts like mutation and fitness particularly interesting. I also took note of how humans are currently generally better than AI at what we take for granted.

CrashCourse AI episode 13.

The next episode promises to cover some ground on how humans and AI might work together.

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Why use games with AI? Games provide training data. An AI can play games or learn from another entity playing games.

Games are so good at training AI with strategies that AI beat human opponents at:

  • Chess in 1997
  • Go in 2017
  • DOTA 2 in 2018
  • Starcraft 2 in 2019

Whatever the game, the central tenet seems to be the minimax algorithm.

Minimax algorithm.

But the flaw on relying only on this algorithm is the premise that you can work out every strategic possibility. As the video explained, the combinations and permutations could outnumber the number of atoms in the known universe. This is not practical or feasible. Instead, an AI might estimate the chances of winning from a smaller set of possibilities.

AI can learn from games and even beat humans because there are clear rules. Where it currently fails to keep up are in areas like humour, social cues, creativity, and surprise.

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