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Neuralink 2022 Update -Human Trials are coming

Let’s get into the latest updates on Elon Musk’s futuristic brain implant company Neuralink. Elon has been talking a lot lately about Neuralink and some of the applications that he expects it will be capable of, or not capable of, in the first decade or so of the product life cycle.

We know that Elon has broadly promised that Neuralink can do everything from helping people with spinal cord injuries, to enabling telepathic communication, curing brain disease like Parkinsons and ALS, allowing us to control devices with our thoughts and even merging human consciousness with artificial intelligence.

But as we get closer to the first clinical human trials for Neuralink, things are starting to become a little more clear on what this Brain Computer Interface technology will actually do, and how it will help people. So, let’s talk about what’s up with Neuralink in 2022.

Neuralink Human Trials 2022

When asked recently if Neuralink was still on track for their first human trial by the end of this year, Elon Musk replied by simply saying, “Yes.” Which I think is a good sign. It does seem like whenever Elon gives an abrupt answer like this, it means that he is confident about what he’s saying.

For comparison, at around the same time last year, when asked about human trials of Neuralink, Elon wrote, “If things go well, we might be able to do initial human trials later this year.” Notice the significant difference in those two replies. Not saying this is a science or anything, but it is notable.

We also saw earlier this year that Neuralink were looking to hire both a Director and Coordinator for Clinical Trials. In the job posting, Neuralink says that The director will “work closely with some of the most innovative doctors and top engineers, as well as working with Neuralink’s first Clinical Trial participants.”

We know that Neuralink have been conducting their surgical trials so far with a combination of monkeys and pigs. In their 2020 demonstration, Neuralink showed us a group of pigs who had all received Neuralink implants, and in some cases had also undergone the procedure to have the implant removed. Then in 2021, we were shown a monkey who could play video games without the need for a controller, using only his brain, which was connected with two Neuralink implants.

Human trials with Neuralink would obviously be a major step forward in product development. Last year, Elon wrote that, “Neuralink is working super hard to ensure implant safety & is in close communication with the FDA.” Previously, during Neuralink events, he has said that the company is striving to exceed all FDA safety requirements, not just to meet them. In the same way that Tesla vehicles exceed all crash safety requirements, they actually score higher than any other car ever manufactured.

What can Neuralink Do?

As we get closer to the prospective timeline for human testing, Elon has also been dialing down a little more into what exactly Neuralink will be able to do in its first phase implementation. It’s been a little bit hard to keep track when Elon is literally talking about using this technology for every crazy thing that can be imagined - that Neuralink would make language obsolete, that it would allow us to create digital backups of human minds, that we could merge our consciousness with an artificial super intelligence and become ultra enhanced cyborgs.

One of the new things that Elon has been talking about recently is treating morbid obesity with a Neuralink, which he brought up during a live TED Talk interview. Which is not something that we expected to hear, but it’s a claim that does seem to be backed up by some science. There have already been a couple of studies done with brain implants in people with morbid obesity, the implant transmitted frequent electric pulses into the hypothalamus region of the brain, which is thought to be driving an increase in appetite. It’s still too soon to know if that particular method is really effective, but it would be significantly less invasive than other surgeries that modify a patient's stomach in hopes of suppressing their appetite.

Elon followed up on the comment in a tweet, writing that it is “Certainly physically possible” to treat obesity through the brain. In the same post, Elon expanded on the concept, writing, “We’re working on bridging broken links between brain & body. Neuralinks in motor & sensory cortex bridging past weak/broken links in neck/spine to Neuralinks in spinal cord should theoretically be able to restore full body functionality.”

Which is one of the more practical implementations of Neuralink technology that we are expecting to see. These electrical signals can be read in the brain by one Neuralink device, and then wirelessly transmitted through BlueTooth to a second Neuralink device that is implanted in a muscle group, where the signal from the brain is delivered straight into the muscles. This exact kind of treatment has been done before with brain implants and muscular implants, but it has always required the patient to have a very cumbersome set up with wires running through their body into their brain, and wires running out of their skull and into a computer. The real innovation of Neuralink is that it makes this all possible with very small implants that connect wirelessly, so just by looking at the patient, you would never know that they have a brain implant.

Elon commented on this in another Tweet, writing, “It is an electronics, slash mechanical, slash software engineering problem for the Neuralink device that is similar in complexity level to smart watches - which are not easy!, plus the surgical robot, which is comparable to state-of-the art CNC machines.”

So the Neuralink has more in common with an Apple Watch than it does with any existing Brain Computer Interface Technology. And it is only made possible by the autonomous robotic device that conducts the surgery, the electrodes that connect the Neuralink device into the brain cortex are too small and fine to be sewn by human hands.

Elon touched on this in a response to being asked if Neuralink could cure tinnitus, a permanent ringing in the ears. Elon wrote, “Definitely. Might be less than 5 years away, as current version Neuralinks are semi-generalized neural read/write devices with about 1000 electrodes and tinnitus  probably needs much less than 1000.” He then added that, “Future generation Neuralinks will increase electrode count by many orders of magnitude.”

This brings us back to setting more realistic expectations of what a Neuralink can and cannot do. It’s entirely possible that in the future, the device can be expanded to handle some very complex issues, but as it is today, the benefits will be limited. Recently a person Tweeted at Elon, asking, “I lost a grandparent to Alzheimers - how will Neuralink address the loss of memory in the human brain?” Elon replied to say, “Current generation Neuralinks can help to some degree, but an advanced case of Alzheimers often involves macro degeneration of the brain. However, Neuralinks should theoretically be able restore almost any functionality lost due *localized* brain damage from stroke or injury.”

So, because those 1,000 electrodes can’t go into all areas of the brain all at once, Neuralink will not be effective against a condition that afflicts the brain as a whole. But those electrodes can be targeted on one particular area of damage or injury, and that’s how Neuralink will start to help in the short term, and this will be the focus of early human trials.

During his TED Talk interview, Elon spoke about the people that reached out to him, wanting to participate in Neuralink’s first human trials. Quote, “The emails that we get at Neuralink are heartbreaking. They'll send us just tragic stories where someone was in the prime of life and they had an accident on a motorcycle and now someone who’s 25 years old can’t even feed themselves. This is something we could fix.” End quote.

In a separate interview with Business Insider that was done in March, Elon talked more specifically about the Neuralink timeline, saying, “Neuralink in the short term is just about solving brain injuries, spinal injuries and that kind of thing. So for many years, Neuralink’s products will just be helpful to someone who has lost the use of their arms or legs or has just a traumatic brain injury of some kind.”

This is a much more realistic viewpoint than what we’ve seen from Elon in interviews of the past. On one episode of the Joe Rogan Podcast, Elon tried to claim that in 5 years from now language would become obsolete because everyone would be using Neuralink to communicate with a kind of digital telepathy. That could have just been the weed talking, but I’m hoping that the more realistic Elon’s messaging becomes, the closer we are getting to a real medical trial of the implant.

And finally, the key to reaching a safe and effective human trial is going to be that robot sewing machine that threads the electrodes into the cortex.  Elon referred to it as being comparable to a CNC machine. Because as good as the chip itself might be, if we can’t have a reliable procedure to perform the implant, then nothing can move forward. The idea is that after a round section of the person’s skull is removed, this robot will come in and place the tiny wires into a very specific areas in the outer layer of the brain - these don’t go deep into the tissue, only a couple of millimeters is enough to tap into the neural network of electrical signals. In theory this can all be done in a couple of hours, while the patient is still conscious - they would get an anesthetic to numb their head, obviously, but they wouldn’t have to go under full sedation, and therefore could be in and out of the procedure in an afternoon. Very similar deal to laser eye surgery - a fast and automated method to accomplish a very complex medical task. 

That’s what this Twitter user was referencing when he recently asked how close the new, version two of the Neuralink robot was to inserting the chip as simply as a LASIK procedure. To which Elon responded, quote, “Getting there.”

We know that the robot system is being tested on monkeys right now, and from what Elon says, it is making progress towards being suitable for human trials.

The last interesting thing that Elon said on Twitter in relation to Neuralink was his comment, “No need for artificial intelligence, neural networks or machine learning quite yet.” He wrote these out as abbreviations, but these are all terms that we are well familiar with from Tesla and their autonomous vehicle program. We know that Elon is an expert in AI and he has people working for him at Tesla in this department that are probably the best in the world. This is a skill set that will eventually be applied at Neuralink, but to what end, we still don’t know.

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Hybrid AI Will Go Mainstream in 2022

Analysts predict an AI boom, driven by possibilities and record funding. While challenges remain, a hybrid approach combining the best of the realm may finally send it sailing into the mainstream.

Artificial intelligence (AI) is becoming the dominant trend in data ecosystems around the world, and by all counts, it will accelerate as the decade unfolds. The more the data community learns about AI and what it can do, the faster it empowers IT systems and structures. This is primarily why IDC predicts the market to top $500 billion as early as 2024, with penetration across virtually all industries driving a wealth of applications and services designed to make work more effective. In fact, CB Insights Research reported that at the close of Q3 2021, funding for AI companies had already surpassed 2020 levels by roughly 55%, setting a global record for the fourth consecutive quarter.

In 2022, we can expect AI to become better in solving practical problems that hamper unstructured language data-driven processes, thanks to improvements in complex cognitive tasks such as natural language understanding (NLU). At the same time, there will be increased scrutiny into how and why AI does what it does, such as ongoing efforts by the U.S. National Institutes of Standards and Technology (NIST) aimed at more explainable AI. This will require greater transparency into AI’s algorithmic functions without diminishing its performance or raising costs.

You shall know a word by the company it keeps

Of all the challenges that AI must cope with, understanding language is one of the toughest. While most AI solutions can crunch massive volumes of raw numbers or structured data in the blink of an eye, the multitude of meanings and nuances in language, based on the context they are in is another matter entirely. More often than not, words are contextual, which means they convey different understandings in different circumstances. Something easy and natural for our brains is not that easy for any piece of software.


This is why the development of software that can interpret language correctly and reliably has become a critical factor in the development of AI across the board. Achieving this level of computational prowess would literally unleash the floodgates of AI development by allowing it to access and ingest virtually any kind of knowledge.

NLU is a vital piece of this puzzle by virtue of its ability to leverage the wealth of language-based information. Language inhabits all aspects of enterprise activity, which means that an AI approach cannot be complete without extracting as much value as possible from this type of data.

A knowledge-based, or symbolic AI approach, leverages a knowledge graph which is an open box. Its structure is created by humans and is understood to represent the real world where concepts are defined and related to each other by semantic relationships. Thanks to knowledge graphs and NLU algorithms, you can read and learn from any text, out-of-the-box, and gain a true understanding of how data is being interpreted and conclusions are being drawn from that interpretation. This is similar to how we as humans are able to create our own specific, domain-oriented knowledge, and it enables AI projects to link its algorithmic results to explicit representations of knowledge.

In 2022, we should see a definitive shift toward this kind of AI approach combining both different techniques. Hybrid AI leverages different techniques to improve overall results and better tackle complex cognitive problems. Hybrid AI is an increasingly popular approach for NLU and natural language processing (NLP). Bringing together the best of AI-based knowledge or symbolic AI and learning models (machine learning, ML) is the most effective way to unlock the value of unstructured language data with the accuracy, speed and scale required by today’s businesses.

Not only will the use of knowledge, symbolic reasoning and semantic understanding produce more accurate results and a more efficient, effective AI environment, it will also reduce the need for cumbersome and resource-intensive training, based on wasteful volumes of documents on expensive, high-speed data infrastructure. Domain-specific knowledge can be added through subject matter experts and/or machine learning algorithms leveraging the analysis of small and pinpointed training sets of data to produce highly accurate, actionable results quickly and efficiently. 

The world of hybrid AI

But why is this transition happening now? Why hasn’t AI been able to harness language-based knowledge previously? We have been led to believe that learning approaches can solve any of our problems. In some cases, they can, but just because ML does well with certain needs and specific contexts doesn’t mean it is always the best method. And we see this all too often when it comes to the ability to understand and process language. Only in the past few years have we seen significant advancements in NLU based on hybrid (or composite) AI approaches.

Rather than throwing one form of AI, with its limited set of tools, at a problem, we can now utilize multiple, different approaches. Each can target the problem from a different angle, using different models, to evaluate and solve the issue in a multi-contextual way. And since each of these techniques can be evaluated independently of one another, it becomes easier to determine which ones deliver the most optimal outcomes.

With the enterprise already having gotten a taste of what AI can do, this hybrid approach is poised to become a strategic initiative in 2022. It produces significant time and cost benefits, while boosting the speed, accuracy and efficiency of analytical and operational processes. To take just one example, the process of annotation is currently performed by select experts, in large part due to the difficulty and expense of training. By combining the proper knowledge repositories and graphs, however, the training can be vastly simplified so that the process itself can be democratized among the knowledge workforce.

More to Come

Of course, research in all forms of AI is ongoing. But we will see particular focus on expanding the knowledge graph and automating ML and other techniques because enterprises are under constant pressure to leverage vast amounts of data quickly and at low cost.

As the year unfolds, we will see steady improvements in the way organizations apply these hybrid models to some of their most core processes. Business automation in the form of email management and search is already in sight. The current keyword-based search approach, for instance, is inherently incapable of absorbing and interpreting entire documents, which is why they can only extract basic, largely non-contextual information. Likewise, automation email management systems can rarely penetrate meaning beyond simple product names and other points of information. In the end, users are left to sort through a long list of hits trying to find the salient pieces of knowledge. This slows down processes, delays decision-making and ultimately hampers productivity and revenue.

Empowering NLU tools with symbolic comprehension under a hybrid framework will give all knowledge-based organizations the ability to mimic the human ability to comprehend entire documents across their intelligent, automated processes.

By , CTO at on March 2, 2022 in Artificial Intelligence