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Last week, The Wall Street Journal reported that a team of researchers in China were treating terminally ill cancer patients with the gene-editing technique known colloquially as CRISPR. According to the Journal‘s report, the Chinese researchers are attempting to halt disease progression in patients with esophageal cancer by tweaking a piece of DNA in some of their white blood cells. This adjustment changes the way their immune system fights the cancer.

But how did China edge out the United States to become the first to use CRISPR in humans? American researchers were, after all, the ones who discovered the techniques’ ability to tweak and alter DNA. But as the Wall Street Journal points out, America is right up on China’s heels. Carl June, a pioneer in immunotherapy cancer treatment at the University of Pennsylvania is awaiting clearance to begin similar trials of his own—using the techniques of CRISPR on immunotherapy (harnessing the immune system to fight cancer)-based treatments for certain malignancies. He might get clearance from the FDA as early as next month.

According to Nature, the American trials will be similar to the newly-approved immunotherapy treatment called Kymriah. The treatment involves removing a person’s blood and isolating their T-cells. Using another form of gene-editing, a disabled HIV virus attaches a receptor to the T-cells (a type of white blood cell). The re-engineered T-cells then grow and proliferate in the lab. When ready, doctors infuse the newly engineered T-cells back into the cancer patient’s body where they go to work finding cancer cells and killing them. While this treatment can be highly effective (often in people who have exhausted every other medical option available), the process itself is extremely time consuming, and it often doesn’t work for everyone. The goal of this new immunotherapy trial involving CRISPR is two-fold: To see if using CRISPR-based therapies is indeed safe for humans and if CRISPR would help make therapies like Kymriah more efficient and effective.

If approved, the new phase one human trial would use CRISPR to tweak the DNA in a person’s T-cells in three ways: The first would do the same as the HIV virus in the Kymriah approach, attaching a receptor that finds cancer. The second would remove a protein that has the potential to mess with the receptor. A third tweak would prevent a cancer cell from finding the T-cell by removing a protein that acts as a tracking device, so to speak, for the malignant cell. If successful, these changes could make immunotherapy far more effective. And, because CRISPR is relatively easy to use in a laboratory setting, it could make treatments that use the process far more efficient, potentially increasing availability and decreasing the time (and money) spent to make the drug.

But China is already so far along, and in some instances, their efforts are showing positive results. If American researchers led the CRISPR discovery and early race, what handicap is allowing China to gain the lead? It’s a little something called the FDA. And it’s worth the lost race. To gain approval for their trial, Chinese researchers had to present their plan to the hospital’s ethics committee. According to the Wall Street Journal, this committee is made up of a handful of the hospital’s doctors, a lawyer, and a former cancer patient. The group discussed the issues for a few hours before they greenlit the human trial.

The FDA’s caution is high for anything that uses gene editing or CRISPR. But other drugs go through an extremely rigorous process, as well. This is all for good reason: Before the FDA existed, manufacturers could market and sell any drug without needing to say what’s in it and without needing to show that it could actually treat the thing that you were buying it to treat. While it’s hard to sit with the idea of potentially losing a medical breakthrough race, it’s important to remember how and why the United States created the FDA in the first place.

Today, we take prescription drugs knowing exactly what the side effects could be and what the general probability of them occurring is. But imagine taking a new drug without knowing these vital pieces of information. Would you still swallow it? We don’t often think about the arduous and long process drugs now go through before they reach our bodies. But these processes are there for an extremely good reason. Consider the fecal transplant. The treatment, which involves collecting stool from a volunteer and administering it to a person with a severe gut infection caused by a bacteria called clostridium difficile, shows immense success, but it is not yet approved by the FDA.

Part of the reason is that the treatment is, like CRISPR studies, entering uncharted territory. There’s so much scientists still don’t understand about the human gut microbiome (the collection of bacteria that live in our intestines) and its effects on our health. Transplanting one person’s gut microbiome into another person’s could indeed cure them of their infection, but it could also cause unwanted consequences. The microbiome has an effect on both our digestion and our immune systems. Theoretically, the recipient of a fecal transplant could be at a higher risk of developing immune system disorders because of the gut microbes they now have. At the same time, just like those cancer patients in the CRISPR immunotherapy trials, C. diff patients are often in a life-threatening situation and the fecal transplant is a last ditch effort to clear up the infection. Researchers admit that it’s hard to know for sure how safe you have to be when these people who could potentially benefit from this treatment are in a life-threatening situation. In the immunotherapy studies, most patients have exhausted all other available treatments.

For a drug to reach your medicine cabinet, it needs to have gone through three phases of clinical trials. Phase one is simply to show that a drug or therapy isn’t toxic. If a drug makes it past that point, it moves on to phase two which it must again maintain that it is non toxic, but also prove that its effective, doing to the job that it’s meant to do. In phase three, researchers must test the drug against the currently available treatment for the condition (if there is one) the new drug is attempting to treat. If a drug doesn’t work any better than one currently on the market and there’s no other redeeming qualities like it being cheaper, or its side effects are less intense, then its much harder for drug companies to gain FDA approval to start selling the drug.

Each step of this process is long and arduous but the hurdles are there for two main and super important reasons: To determine what the drug’s toxicity (in other words, what are its chances of killing you or causing other forms of short and long term damage to your body?) and, does it actually do what it says its supposed to do? If a drug is supposed to reduce the symptoms of heartburn, does it actually do that? This all seems obvious that this kind of testing should exist. But the only reason it does, is because there was a time when it didn’t.

Back in 1906, the United States Congress passed the original Food and Drug Act (the precursor to today’s Food and Drug Administration). At that point, the law’s main purpose was to prevent the buying and selling of food, drinks, and drugs from having any form of mislabeling or tainting. Simply, the product had to contain what it said it contained on the label.

The current regulations governing the FDA’s testing process are a product of our own mistakes. In 1937, as soon as a drug called Elixir Sulfanilamide reached the market, it quickly caused the deaths of 107 people, many of whom were children. The active ingredient in the drug, sulfanilamide, was used at the time as a type of antibiotic used to treat anything from gonorrhea to strep throat. The drug originally came in the form of a pill. But one pharmaceutical company, the S.E. Massengill Company, decided that the therapy would be even more popular if it came in the form of a flavored liquid. So they had a chemist mix sulfanilamide, with diethylene glycol, and water—plus a little bit of raspberry flavoring. Once ready, they labeled it accordingly, and distributed gallons of it across the country—and pharmacies readily purchased it.

Diethylene glycol is highly miscible, that is, it’s great at mixing together any particle into a well-formed liquid. As such, the new formulation was deemed a great success. Until people started actually taking it. Turns out that in addition to its high miscibility, diethylene glycol is also extremely poisonous to humans, and causes immediate acute kidney failure. Death reports soon started coming in of people who had taken the liquid medicine. It was swiftly taken off the market. The thing is, the drug went immediately from the laboratory to the medicine cabinet. No testing beforehand whatsoever. So, the following year, in 1938, the United States passed the The Federal Food, Drug, and Cosmetic Act, which required that new drugs had to show that they were safe before they started selling them—essentially what phase one in the clinical trial approval process is today. This began an entirely new wave of regulations, each year bringing us one step closer to the highly arduous process we have today. Regrettably, sulfanilamide wasn’t the only infamous incident that tweaked this regulatory process. Other tragedies over the past century have shaped it as well.

In the now infamous case of thalidomide of the 1960s, the drug (thalidomide) was a sleeping pill that quickly became widely popular in Germany. Soon after Australian doctors discovered that it could also alleviate the nausea caused by morning sickness in pregnancy. So doctors started to prescribe the drug, off-label (a practice still very much in use today) to pregnant women. But while there had been some testing done beforehand to determine the drug was safe for humans to take, no one had done any studying of the drugs effects on a developing fetus. As the world soon found, it can cause severe birth defects; specifically causing the shortening or complete absence of limbs. This, in part, led the U.S. to create much more stringent laws around drug dispensing, requiring drug makers to prove their drug works before it can gain FDA approval.

We have birth control to thank for laws mandating that drugs come with patient packet inserts listing every side effect available and the chances of each one occurring. Initially birth control was almost taken off the market due to its dangerous side effects. But rightfully, women pressed that they wanted to be given a choice first, whether they would agree to taking a drug with the risk of whatever side effects. Today, that’s typically how drugs are presented by physicians to their patients—weighing the benefits versus the risks, which are clearly and accurately made available, and allowing the patient to choose.

Learning from our past, it is wise that we remain cautious and go through the regulatory procedures that have been a century in the making.

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Gato Might Not Be The Sharpest In Agi Race: Looking Back At Alphazero

It’s time to ask if Gato has a better chance is being an AGI than AlphaZero.

Gato, as the agent is known, is the generalist AI of DeepMind that can execute a wide range of jobs that humans can, without specializing in a single skill. Gato can do over 600 various things, including play video games, caption photos, and move real-world robotic arms. It is a generalist policy that is multimodal, multi-task, and multi-embodiment.

How Does Gato Work?

Gato operates by normalizing and modulating all the inputs and data streams from various jobs into flat token sequences. It can interact with languages, and images, play games and interact with mechanical objects when treated as weights. It is accomplished by sampling the tokenized weights from the first step into autoregressive action vectors one token at a time. The action is decoded and delivered to the environment, producing a new observation, when all tokens composing the action vector have been tested (as stated by the environment’s action specification). After that, the method is repeated. Within its 1024-token context window, the model is always aware of all previous observations and actions. Gato’s main design principle is to train on as many different types of data as possible, including photos, text, proprioception, joint torques, button presses, and other discrete and continuous observations and activities. It serializes all data into a flat series of tokens to facilitate the analysis of this multimodal input. Gato can be trained and sampled from this representation in the same way that a normal large-scale language model can. Sampled tokens are constructed into dialogue responses, captions, button presses, and other actions based on the context during deployment. The tokenization, network design, loss function, and deployment of Gato are described in the subsections below.

Is It Being Overhyped?

While Gato is undeniably fascinating, some researchers have gotten a bit carried away in the week since its release. One of DeepMind’s top researchers and a coauthor of the Gato paper, Nando de Freitas, couldn’t contain his excitement. “The game is over!” he tweeted, suggesting that there is now a clear path from Gato to artificial general intelligence, or AGI, a vague concept of human- or superhuman-level AI. The way to build AGI, he claimed, is mostly a question of scale: making models such as Gato bigger and better. Unsurprisingly, de Freitas’s announcement triggered breathless press coverage that DeepMind is “on the verge” of human-level artificial intelligence. This is not the first-time hype has outstripped reality. Other exciting new AI models, such as OpenAI’s text generator GPT-3 and image generator DALL-E, have generated similarly grand claims. For many in the field, this kind of feverish discourse overshadows other important research areas in AI.

Is Gato better than Alphazero?

The strength of “Gato” lies in one key thing: it never forgets what it has been taught.

In recent years, many AI models have begun to combine different skills. Examples include “DALL-E” or “Imagen,” capable of generating images from a simple text description. Recently, the French artificial intelligence model “NooK” managed to beat several world bridge champions.

“AlphaZero,” another model already built by Deepmind, has learned to play Go, chess, and shogi. But there’s one difference: “AlphaZero” could only learn one task at a time. After learning to play a strategy game, it had to forget what it had learned to move on to the next game.

What is AlphaZero? Can AlphaZero be Called AGI?

In simple words, the answer will be a ‘no’. In the race to achieve AGI, AlphaZero can only be considered a narrow superintelligence in that it exceeds all human performance in a single problem. However, AGI does not only consist of superintelligence as it is not only about surpassing human task abilities but rather also requires the concept of general intelligence or what can be called common sense.

Ai Might Be The Future. But Can It Also Predict It?

We all know that artificial intelligence has been the hot topic of recent times and that it has been adopted by companies covering virtually every sector. So these days, when you’re dealing with an automated answering service, it’s highly likely that there is no human intervention at all. This trend is set to continue and reach into many new areas. But there are even more exciting prospects ahead that suggest that the algorithms used in AI may soon start to enable increasingly accurate predictions of future events. Indeed, this is something that has also started to occur – and in some quite surprising ways. The theory behind it is quite simple: by combining the processing of big datasets and using the algorithms that drive AI, an ever-more accurate model of future events can be created. One of the leading exponents in this reasonably new field is

We all know that artificial intelligence has been the hot topic of recent times and that it has been adopted by companies covering virtually every sector. So these days, when you’re dealing with an automated answering service, it’s highly likely that there is no human intervention at all. This trend is set to continue and reach into many new areas. But there are even more exciting prospects ahead that suggest that the algorithms used in AI may soon start to enable increasingly accurate predictions of future events. Indeed, this is something that has also started to occur – and in some quite surprising ways. The theory behind it is quite simple: by combining the processing of big datasets and using the algorithms that drive AI, an ever-more accurate model of future events can be created. One of the leading exponents in this reasonably new field is Black Swan Technologies whose clients include Disney. Among the work they have carried out for the entertainment giant was predicting the likely popularity of Frozen before release. By looking at the data around similar animated titles as well as examining popular genres and types of film on YouTube it enabled them to accurately gauge even how many copies would be sold once the movie was released on DVD. Retailers have also been quick to start experimenting with Black Swan’s technology with supermarkets using it to project just when and how many of their customers will be choosing to barbecue on any given weekend. Western Canada Fashion Week 2023 Spring ” ( CC BY-SA 2.0 ) by IQRemix The fashion industry is also one that is always looking towards what are going to be the trends for future seasons. Again, AI is being used to help these predictions to become more accurate. This is facilitated by a system that scours social media and e-commerce sites to analyze colors, sizes and patterns that appear most frequently and which may be pointing towards the direction that styles may be headed. On a less frivolous note, healthcare is another sector in which the ability to predict future situations is being trialed. One of the best-known exponents is Diagnostic Robotics , a business founded and headed up by Kira Radinsky Ph.D. with offices in Tel Aviv and New York. The fact that much of medicine is already so heavily statistics-based is obviously useful, but it needs AI to add that extra dimension. Trading in stocks, shares and currencies is another area in which being able to accurately predict the future can also mean the difference between success and failure. More and more trading platforms appearing and being listed on sites like Stockapps UK . There is already plenty of choice and most offer extensive support. The above site rates the platforms according to a vast array of key features, such as commission or products offered, such as CFD trading. It’s therefore logical to imagine that one day AI support could number among those factors. This would obviously give an edge over the competition – it could well take many providers to the next level. So we can confidently predict that AI will become increasingly effective in defining what the future holds in a wide variety of ways. And, when it does reach its full potential, it will create a very different world indeed.

Failure And Resilience Might Be The Best Teachers

Successful Thinking: Learning from past failures and using personal intuition are crucial factors of success, says L’Oréal Australia and New Zealand CEO Rodrigo Pizarro.

The past few years have been difficult for companies and leaders globally. However, for L’Oréal Australia and New Zealand CEO Rodrigo Pizarro, his family background of entrepreneurship and resilience in Portugal helped him to not only pivot the cosmetic and skincare company’s focus during the global pandemic, but to significantly grow the company.

Pizarro, who has led the local arm of the global cosmetics and its 40 brands for eight years, says during the pandemic, listening intently to his employees and customers and acting promptly “ahead of the trends” was an important part of the company’s success.

Under Pizarro’s leadership, L’Oreal has transformed into a leading digital business, with a strong focus on data analytics and AI. Every employee at the company in Australia and New Zealand has undergone a data literacy program to enable them to make data-centric decisions, Pizarro says.

L’Oreal Group, the French parent company of L’Oreal Australia and NZ, reported revenue of 18.6 billion euros ($28.5 billion) for the first six month of 2023, an increase of almost 20% on the previous corresponding period. In Australia, the company experienced double-digit growth in the second quarter of 2023.

What was the most formative thing in your childhood that made you want to succeed in business?

Seeing my father’s journey over his working years. Seeing him succeed, fail, succeed once more, only to fail again three times. The first two times as a senior executive in two different organisations, the third while running his own business.

I have learned from all moments, but mostly from his failures. I have learned about resilience – it’s not how you fall but how you get back up. I learned about trust, or better, when not to trust.

How have you managed to build L’Oréal Australia and its stable of around 40 brands despite a challenging market?

First and foremost, I have surrounded myself with an incredible team of talented people who are skilled in their respective fields and positions. As the CEO, it’s my job to ensure that our people are nurtured professionally and personally. For me, it’s about keeping my eyes on the future – anticipating and creating a vision of what will keep us at the forefront of the market. A few imperative things that come to mind include our strong focus on data and analytics, growth of technological and digital innovations … and of course, robust sustainability initiatives.

When choosing what trends to back, what’s your decision-making process?

Trends, by definition, come and go. Yes, we can surf them on the short term, but to be successful long-term we need to identify what the behaviours are that are here to stay and over-invest in them. To establish what these are, we need to be constantly listening to our consumers and employees and tuned in to the overall vision of the industry and key opinion leaders within it. Lastly, we all need a little bit of luck.

“I have learned about resilience – it’s not how you fall but how you get back up. I learned about trust, or better, when not to trust.”

– L’Oreal Australia and NZ CEO Rodrigo Pizarro

What characteristic of yourself do you think is underrated by other people?

I have a very strong vision and intuition about business growth drivers. I can identify quickly, in different circumstances … major opportunities for growth and the developments needed to drive significant transformations that will allow that growth.

This skill is also very useful in moments of crisis where I can keep my cool and see the ways out of it, anticipate and commit in decision making.

This was the case through Covid, focusing on people and making successive decisions initially to ensure their health and safety, but later to perfectly balance the employee wellbeing, motivation and productivity.

Is there anything in your daily routine that keeps you sharp, sane and motivated?

Exercise. I start my day in the gym … to release adrenaline and get my body and mind ready for the everyday challenges of life. In my personal time, I like going on bicycle rides with my son, and being active by enjoying sports together.

If you had $10,000 to invest, perhaps for a niece of nephew, where would you invest it?

I like to take risks, so I would invest if in NFT, or property in the Meta world – it’s the future.

This is an edited version of the conversation.

Fuser Might Be The Music Game For A Post

FUSER might be the music game for a post-music game era

At this point, it’s definitely safe to say that the era of rhythm and music games sparked by Guitar Hero is well and truly dead. We haven’t seen a Guitar Hero title in five years, nor have we seen a proper Rock Band release in three. When you consider that we used to get multiple Guitar Hero games each and every year, it’s safe to say that we’ve moved beyond that particular craze.

Music and rhythm games are still around, no doubt, but they’re decidedly less mainstream than they were 10 years ago. FUSER, an upcoming game from Harmonix that puts you in the shoes of a festival DJ, could very well change that.

Last week, I sat down with some folks from Harmonix (virtually, of course), and they gave me a closer look at the game before providing me with a demo to play myself. It’s safe to say that I understand FUSER now – I get what it’s trying to achieve and I’m sold on the idea. Though what I’ve played myself amounts to a small portion of the finished game, the content I got to see and demo shows a lot of promise and suggests that FUSER could be a music game for a mainstream crowd that is largely over music games.

On the surface at least, FUSER feels like a game that’s less about developing a skill – learning to play your instrument as you progress through the ranks, like in Rock Band – and more about being creative. That’s because you’re not trying to play along with someone else’s song. Instead, you’re taking bits and pieces of different songs and putting them together to make your own mix, just as a festival DJ would.

Before you start a performance, you get to pick which songs go into your crate. The demo build I had the chance to play only had a portion of the songs that will be in the finished game, but Harmonix is actually announcing a new collection of songs today – the new additions and the list of all the songs that have been revealed up to this point can be seen in the galleries below. As you play, you can drop parts from each of those songs on the turntable that’s in front of you, which has space for four different discs.

You could, for instance, group the bassline from The Weeknd’s Blinding Lights, the synth from Donna Summer’s Hot Stuff, the horns from Amy Winehouse’s Rehab, and the vocals from Grouplove’s Tongue Tied to create your own mix. You’re not just limited to pulling various parts from famous songs either, as you can apply filters to each part, change the BPM of your mix on the fly, or even create your own loops using various instrument samples and drop them into your song.

As someone who played so much Rock Band and Guitar Hero that I could play most songs on expert and who also spent an obscene amount of time and money grinding Dance Dance Revolution back in my younger days, I’m not really a stranger to rhythm games, but that doesn’t really help me here. Dropping discs with the downbeat is an important aspect of playing this game, so experience with rhythm games will help in that regard, but success in this game isn’t all about timing. You also have to please the crowd, and you can do that by granting requests throughout your performance.

Maybe someone wants to hear a guitar part – any guitar part – or another person wants to hear Shania Twain. You’ll only have a few seconds to grant those requests, and if you do, the crowd will like you more. Ignore the crowd’s wishes or mess up your timing with disc drops and the crowd’s opinion of you will drop, eventually ending your performance early if it gets too low.

Harmonix told me that in FUSER, the idea isn’t to reward players who create the “correct” mixes and penalize those who don’t. In Rock Band, the failure condition is clear because the song you’re playing should really only be played one way; mess up enough notes and you fail the song. What makes a good mix is entirely subjective though, and what sounds good to me might sound awful to you. So, instead of failing because you mixed the wrong kind of song, you’ll fail when you don’t please the crowd or pay attention to the timing of your drops.

Aside from goals that you’ll be presented with throughout the song (many of which aren’t very specific), you’ve got a lot of freedom in how you create your mix. That kind of experimentation might be the most addicting part of FUSER, and I can see it being just as replayable as Rock Band or Guitar Hero but for entirely different reasons.

I have no qualms telling you that I’m completely awful at this game, as I failed two of the three stages that were available to me in this demo multiple times before finally succeeding. You have a lot of options when it comes to creating your mixes, and while that can ensure that you’ll be doing something unique each time you play a stage, it can also be overwhelming. Just like in Rock Band or Guitar Hero, it seems that practice will make perfect in FUSER.

Of course, the single-player campaign is just one part of FUSER, though it’s the part I spent the most time with in the demo. There will also be competitive and collaborative multiplayer modes, a freestyle mode that gives you a bit more freedom in experimenting with songs, and even a social aspect that lets you share the mixes you create and follow other players so you can see their mixes too. There also seems to be a lot of options when it comes to customizing your DJ, with a ton of choices for clothes, makeup, hairstyles, and facial features.

All in all, I’m impressed by what I’ve seen of FUSER so far. There’s certainly a lot to take in, but this could be one of Harmonix’s most accessible games for the simple fact that it doesn’t require a bunch of different peripherals – all you need to play is a controller or a keyboard and mouse (and the game, of course). I’m looking forward to seeing the full game in November, because at the moment FUSER shows a lot of promise.

We Finally Have The Juno Spacecraft’s First Results On Jupiter

Last summer, the Juno spacecraft flew within about 2,600 miles of Jupiter—the closest any human-made object had ever come to the largest planet in our solar system.

Scientists are still analyzing all the juicy data Juno collected during that first flyby, as well as later orbits, but the first results have just been published. Two new studies in Science and 44 papers in Geophysical Research Letters document a number of odd and amazing findings. Here are the highlights of what we’ve learned so far about Jupiter.

Its north pole is a chaotic mess of storms…

Juno gets 10 times closer to Jupiter’s north pole than any other spacecraft in history. Images from its first close pass show the tumultuous region is dotted with oval-shaped cyclones, which span as much as 870 miles across. That’s wider than the distance between Chicago and New York.

…And it’s very different from Saturn’s north pole.

Saturn’s north pole is encircled by an enormous hexagon-shaped storm, with a high-speed vortex spinning at its center. Jupiter’s north pole is not nearly so organized, showing that the atmospheres of these two gas giants are fundamentally different.

Jupiter may not have a distinct core.

“We used to think there was like a little ball of heavy elements, small and quite distinct at its center,” says NASA astrophysicist Jack Connerney. “Now we’re thinking that mass may be much more spread out.” High heat and pressure at Jupiter’s center may be dissolving the planet’s original rock-ice core in a layer of liquid metallic hydrogen, eroding it until it’s no longer sharply differentiated from the rest of the gas giant.

Its atmosphere circulates like Earth’s…

Peering into the thermal structure of Jupiter’s atmosphere, Juno found signs that ammonia wells up from the deep atmosphere, feeding clouds that form giant weather systems around its equator. These “striking and unexpected” features resemble Earth’s Hadley cells, wherein winds blowing toward the equator rise, produce thunderstorms, and then flow back toward the poles. But Jupiter’s cells are much bigger, and instead of water, they rain out ammonia crystals that quickly evaporate.

…But its auroras are not like ours.

Juno found that the electrons in Jupiter’s auroras mostly stream upwards, away from the poles and toward space. If Jupiter’s auroras were like Earth’s, Juno would have seen more electrons flowing down as well. “We’ve had the electrons going in the wrong direction this whole time,” says Connerney. “And that’s kind of the theme, here—we’re finding out that a lot of our simple interpretations about Jupiter don’t really hold.”

Its magnetic field is twice as strong as we expected…

Juno found that, close up, Jupiter’s magnetic field is roughly 10 times stronger than Earth’s.

…And its dynamo might be showing.

For hundreds of years, scientists have wondered how planets and stars generate magnetic fields. On Earth, we can’t see the dynamo that’s generating our magnetic field because it’s buried deep in a rocky, iron-laden crust. But that’s not a problem with a gas giant. Jupiter’s magnetic field is turning out to be a lot more complicated than expected, with lots of small-scale structures embedded. According to Connerney, these variations may mean Juno is getting close to the dynamo, and that Jupiter’s dynamo is very close to the surface. By piecing together data from one orbit at a time, Juno may provide the first clear map of what a dynamo looks like.

What’s most exciting?

Connerney thinks the magnetic field findings are the most exciting so far. “After 500 years of wondering,” he says, “we might actually see what a dynamo looks like by the end of the mission.” But he admits that as a magnetic field scientist, he’s biased. The other teams of researchers are equally excited about their own findings, he says. “It’s like six blind guys telling you what an elephant looks like. It just depends on which part you’re grabbing at that point.”

Juno still has another year or two before it retires, with no doubt its biggest discoveries yet to come. By the end of it, we should have a much more complete picture of the elephant in the solar system.

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