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Posts Tagged ‘intelligence

Here is a phrase uttered and written so much that it has practically become a trope: Beware, robots will take our jobs.


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Technology-enabled automation has always taken away old jobs, e.g., we do not need phone operators to manually connect us. But people conveniently forget how automation also creates new jobs, e.g., maintainers and improvers of phones. To that end, the video featured a truck driver whose duties evolved along with the development of automated truck-driving.

The automated truck-driving segment ended with the test driver stating that AI was not making people redundant. It was doing jobs that people no longer wanted to do.

The next video segment featured an automated sea port that moved the containers that arrived in ships. The repeated theme was that the human responsibility shifted from moving the containers to maintaining the robotic cranes and vehicles that moved the containers.

An important concept from both segments was that current AI might have good specific intelligence, but it has poor general intelligence. If an environment is controlled or if the problem is structured, AI is often safer, more efficient, and more effective than people.

The final video was about a chain’s pizza order prediction, preparation, and delivery. It emphasised how humans and AI work together and countered the popular narrative of AI taking humans entirely out of the equation.

The underlying message was that people fight change that they do not like or do not understand. This is true in AI or practically any other change, e.g., policy, circumstance, practice.


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This episode of the YouTube Original series on artificial intelligence (AI) was way out there. It focused on how AI might help us live on another planet. What follows are my notes on the episode.

If NASA’s plan to send humans to Mars by 2033 is to happen, various forms of AI need to be sent ahead to build habitats for life and work.

Current construction relies on experience and historical knowledge. AI-enabled construction (AKA generative design) compares and predicts how different designs might operate in Mars.

Side note: Closer to home, generative design also helps us make predictions by answering our what-if questions. What if this structure is placed here? What if there is more of them?

Other than modelling possibilities, AI that builds must not just follow instructions but also react, make decisions, and problem-solve. A likely issue on Mars is using and replenishing resources. One building material is a biopolymer partly synthesised from maize. If AI is to farm corn in Mars, what might it learn from how we do it on Earth?

The video segued to the Netherlands which has the world’s second-highest fresh food production despite its small size. It owes this ability in large part to the AI-informed agricultural techniques developed at Wageningen University.

Most folk will probably relate to how developing AI for Mars actually helps us live life better on earth. It has the capacity to help us think and operate better in terms of how we consume and deploy resources. Imagine how much the rest of the world would benefit from scaling up the techniques developed in the Netherlands.


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Can artificial intelligence (AI) emote or create art?

Perhaps the question is unfair. After all, some people we know might have trouble expressing their emotions or making basic shapes.

So it makes sense to see what something fuzzy like emotions might consist of. The components include the meaning of words, memory of events, and the expression of words. If that is the case, modern chat bots fit this basic bill.

On a higher plane are avatars like SimSensei that monitor human facial expressions and respond accordingly. Apparently it has been used in a comparative study for people suffering from PTSD. That study found that patients preferred the avatar because it was perceived to be less judgmental.

And then there are the robot companions that are still on the creepy side of the uncanny valley. These artificial flesh and no blood human analogues look and operate like flexible and more intelligent mannequins, but it is early days yet on this front.

As for whether AI can create art, consider Benjamin, an AI that writes screenplays. According to an AI expert, Pedro Domingos, art and creativity for an AI is easier than problem solving. AI can already create art that moves people and music that is indistinguishable from that of human composers.

The video does not say this, but such powerful AI are not commonplace yet. We still have AI that struggles to make sense of human fuzziness.

The third and last part of the video seemed like an odd inclusion — robot race car drivers. Two competing teams tested their robo-cars’ abilities to overtake another car. This was a test of strategic decision making and a proxy for aggression and competitiveness.

Like the previous videos in the series, this one did not conclude with firm answers but with questions instead. Will AI ever have the will to win, the depth or create, the empathy to connect on a deep human level? If humans are perpetuated biological algorithms, might AI evolve to emulate humans? Will they be more like us or not?


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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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The second episode of the YouTube Original series on artificial intelligence (AI) focused on how it might compensate for human disease or conditions .

One example was how speech recognition, live transcription, and machine learning helped a hearing-impaired scientist communicate. The AI was trained to recognise voice and transcribe his words on his phone screen.

Distinguishing usage of words like “there”, “their”, and “they’re” required machine learning of large datasets of words and sentences so that the AI learnt grammar and syntax. But while such an AI might recognise the way most people speak, the scientist had a strong accent and he had to retrain it to recognise the way he spoke.

Recognising different accents is one thing, recognising speech by individuals afflicted with Lou Gehrig’s disease or amyotrophic lateral sclerosis (ALS) is another. The nerve cells of people with ALS degenerate over time and this slurs their speech. Samples of speech from people with ALS combined with machine learning might allow them to communicate with others and remote control devices.

Another human condition is diabetic retinopathy — blindness brought on by diabetes. This problem is particularly acute in India because there are not enough eye doctors to screen patients. AI could be trained to read retinal scans to detect early cases of this condition. To do this, doctors grade initial scans on five levels and AI learns to recognise and grade new scans.

This episode took care not to paint only a rosy picture. AI needs to learn and it makes mistakes. The video illustrated this when Google engineers tested phone-based AI on the speech patterns of a person with ALS.

Some cynics might say that the YouTube video is an elaborate advertisement for Google’s growing prowess in AI. But I say that there is more than enough negativity about AI and much of it is based on fiction and ignorance. We need to look forward with responsible, helpful, and powerful possibilities.


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Would you take anything about artificial intelligence seriously if it was delivered by Robert Downey Jr (aka Tony Stark aka Iron Man)?

Well, he is the host a scripted eight-part documentary series, so the authenticity and accuracy of the content is subject to whoever curated and connected the most current information. The series is a “YouTube Original” but there is scant information beyond that.

The first episode focused on the development of digital consciousness, affective (emotional) computing, and human augmentation. The examples explored in this episode included a digital child (BabyX), customer service avatars, and advanced prosthetics.

One of the most important concepts to that a layperson might take away from the episode is that AI is not an independent and all-powerful entity. The best AI now is a combination of human and machine with the latter modelled on the former.

The other concept of capturing, augmenting, and improving upon human intelligence is how far we should go. This is the same question with another technological development — DNA manipulation.

The series seeks like a very promising one and I hope to catch the remaining episodes.


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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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