The source article is in Chinese. Click the picture to be directed.

We have summarized a very interesting article that highlights the most recent AI development as well as its future. Most of the information and comments are from this link which we translated. AI will become very powerful in every aspect, so the question for humans is how to apply it best in our world.

The next wave of AI: Studying the human brain 

This year’s Nobel Prize in Physics was won by Hopfield and Hinton. They used physics knowledge and tools to respectively invent the well-known artificial neural network, The Hopfield network and the Boltzmann machine, laying an important foundation for machine learning technology and promoting the development of AI.

Artificial neural networks are technologies that imitate the operation of the human brain. Hinton said that if we can understand how the brain adjusts the strength of connections between neurons, we can create amazing AI systems like GPT-4. AI will definitely be better than humans. It will be more intelligent, have a wealth of knowledge, but require far fewer neural connections than humans.

Hassabis, who jointly won the Nobel Prize in Chemistry with Baker and is the founder of Google DeepMind, believes that the next stage of research can use AI models to analyze the human brain to promote progress in the field of neuroscience. “I think this is a complete cycle. Neuroscience kind of inspired modern AI, and then AI will come back to help us understand what’s special about the brain.”

Can AI knowledge surpass that of humans?

When asked whether or not AI will be able to surpass the knowledge of humans, Hinton said that he believes we’ve already been surpassed. He added that though AI still has the problem of fabricating facts, also known as hallucinating, it doesn’t change the fact that it still has a greater range of knowledge than any human being.

When asked for his comment, Nobel Prize Winner in Medicine, Ruvkun, said that he believes human capabilities have been overestimated, and humans are just as liable to create false information like AI does. This is especially true with the rise of social media, which has only made the situation more serious. He concluded his thoughts by saying that it’s necessary for AI to reach the level of human beings, but it’s not a level that’s too difficult to reach.

Hassabis was also in agreement, and said that AI capabilities will advance rapidly in all aspects. Consequently, it is very important to think about how humans will design the AI systems and how they are applied in the real world.

What is a Hopfield Network?

A Hopfield network is a type of recurrent artificial neural network, often considered a “content-addressable memory,” which is designed to store patterns and can retrieve a complete pattern even when given a partial or noisy input, essentially acting like an associative memory system by minimizing an “energy function” to reach stable states; named after its inventor, John Hopfield.

Structure:

A single layer of neurons where each neuron is connected to every other neuron in the network, with symmetric connections (meaning the weight from neuron A to B is the same as the weight from B to A).

Function:

By adjusting the connection weights, the network can store patterns, and when presented with a partial or noisy version of a stored pattern, it will iteratively update its state to converge towards the closest stored pattern.

Energy Minimization:

The network operates by minimizing an “energy function,” where each state change reduces the energy, eventually leading to a stable state representing a stored pattern.

Applications:

Image restoration, pattern recognition, optimization problems, and understanding associative memory in the brain.

We hope this has been an interesting read. Now, what’s next? We look forward to seeing you in the next blog!

Video link: https://youtu.be/dATBteNQ-zY?si=bgPDXw-3FblyfnaW

Picking up where we left off on our previous behind the scenes diversity blog, we are certain that there is one thing that AI has yet to really accomplish. AI’s inability to generate something unique in music and in interpretation is something interesting to explore.

We have started to show the video, Interpreter and Music, to our closer circle of colleagues and friends in order to gather feedback. One colleague sent us a video from Kuban Music on YouTube about AI singing a Chinese song. Our initial reaction was of surprise at how impressive it is that an AI was able to sing like that. Our director of translation Mei-Ling thinks it’s so cool that she started to learn the song from the AI. But the more we listen to it, the more we begin to realize that there’s something lacking in its composition and performance. After repeated listening, Mei-Ling, who is not even close to be called a singer, feels the urge to record herself to challenge the AI singer. She said it was a therapeutic experience, because she feels that she has something unique that the AI is lacking — heart and liveliness. There is something special from hearing a human singing that an AI voice couldn’t produce. For comparison, please see both the AI and the human samples below. We are sure if we were to invite 100 humans to sing, they will come out all differently with their own individual uniqueness, while AI variations will most likely be limited.

Everyone knows that AI is a result of big data, “aggregating” from others to generate its materials. It’s trained in a way to mimic humans. Notice that’s humans, plural. This is because it’s not trained to mimic individual people, but a general populace. It essentially crowdsources behavior from the resources it’s trained on, and becomes not just one person, but many. Though the programmers may have the AI come up with clever phrases, such as AI shedding tears, to make it sound like its own person, it isn’t nearly as unique and diverse as humans. It’s inherently impossible for that to happen because of the origin of the AI’s nature.

On the contrary to that, with our interpreter and music video, individuality is one thing that we really wanted to express. 15 individual contributions from around the world bring in incredibly vast diversity and inclusion, yet no interpreters were instructed on what exactly to be contributed. Every piece comes out of interpreter’s individuality with each interpreter’s own language, culture, thoughts, feelings, circumstances and education that leads to how they act. In this sense, everyone is as unique as a fingerprint—there really is nobody like each one of them in the world.

For music, it’s always about expressing oneself with human emotions and energy, and AI may be struggling to do that because it doesn’t have a sense of self. The ability to have your emotions flow out is something that AI is not able to replicate, at least not in present times. AI is always searching for the “right” answer, but there isn’t one with creative media like music or abstract concepts like human energy.

AI is unable to think outside of the box and improvise like humans can. They’re trained to think on pre-existing thoughts and opinions. Anything outside of that scope is not something that they are able to even perceive. That’s why, for example, AI will never truly be able to replicate jazz. Jazz is a collaborative performance where innovation and creativity are key elements. At any given minute, the tempo and melody can change to something entirely different, and it’s up to the musicians to keep up and complement each other so that it doesn’t sound like complete chaos. AI is very exciting technology, but there’s nothing quite as thrilling as watching jazz musicians collaborate or excellent interpreters work.

More often than not, interpretation is similar to Jazz where interpreters often need to improvise and come up with creative solutions to their on-the-spot translation. AI is able to follow patterns to come up with solutions that others have thought of and have done before, but they’re not able to come up with these types of originality their own. AI singing or interpreting at the current stage may serve as a gist of ideas, a prototype, or a machine for learning a song. Still, it will be important for humans to intervene, and add the kind of individuality or human touch that will help it sparkle and make it shine and standout as a piece of unique individual work.

In the future, the methodology used to create AI may change. Is it possible that AI will get closer to having more individuality and uniqueness? We’d love to hear our colleague’s thoughts on the topic because everyone brings their own unique perspective. There are multiple answers to this question and we’re happy to start the discussion and see where it leads us since this is such an important topic in this day and age!

AI singing sample: (Listen Here)

Human singing sample: (Listen Here)