For many science fiction fans the computer voice on the Enterprise is iconic, and is, in fact, “the” voice of any computer. Majel Barrett deliberately gave the computer a stilted, monotone, mechanical voice to reflect the fact that it was a non-sentient machine. But also, it reflected the technology at the time. Voice synthesizers had been around since the VODER, first debuted at the 1939 World’s Fair. They produce an electronic-sounding definitely artificial voice. Many people are also familiar with this electronic voice through Stephen Hawking’s voice synthesizer.

But now we have the technology to create artificial speech that sounds convincingly human – the voice sounds natural and not electronic and contains all the inflection and nuance of natural speech. Sesame is one example, and you can try the demo for yourself. The app adds pauses, breaths, “ums”, and other fillers to sound more natural. Essentially it is now simply not possible to tell an AI generated voice from a human generated voice. I recently rewatched Star Trek TOS and paid close attention to the futurism in the series and was struck by how dated all concepts of AI were in the show. We can already produce natural-sounding human speech, so why is the Enterprise’s computer so mechanical 200 years in the future?

However – there is another explanation (this is obviously a retcon having nothing to do with the original intent of the creators). More recently it occurred to me that we might consider the mechanical nature of the computer voice a deliberate feature, not a bug of technological limitations. In the episode Tomorrow is Yesterday the ship’s computer had recently been overhauled on Cygnet XIV and was given a female personality with a more natural sounding (albeit humorously annoying) voice. We can therefore say that the technology existed and the mechanical voice was at least partly a deliberate choice. At this point you are probably thinking, “So what?”

Well, one of the features of LLM-based chatbots is that they are getting increasingly good at imitating human interactions through language, whether with text or speech. This creates the powerful illusion that they are sentient and feeling, when they clearly are not. I know this point can be debated – how can we be sure they are not sentient? I maintain they are simply not built to be sentient. As an example, the same technology can be used to make pictures, but no one argues that Midjourney is sentient. So for the sake of discussion let’s stipulate LLMs are not sentient.  Continue Reading »

Solar power as a source of electricity has been on a meteoric rise, and has crossed some encouraging milestone recently. Every way you look at it, solar is booming. This is driven primarily by the decrease in the cost of adding solar power, so let’s start there.

The Berkley Lab has been tracking the cost of solar power in the US for years, so they are a reliable source of information. They get direct data from the actual price paid by consumers, and break down the cost of panels, the cost of installation, and all the “soft” costs that  are part of the industry. In 2009 the total cost of installing solar between $7 – 8.70 per watt. An average US residential home installs a 6-8 kW system, with the average increasing over the years. In 2025 the average size was 7.7 kW. Let’s use a 7 kW system, at $8 per watt, that’s $56,000 total installation cost. Today the average price of installed residential solar in the US is $3.6 per watt (but it is $3.0 if you pay upfront, $4.50 if you finance). So that same 7 kW system now costs $25,200. (If you pay for it outright, the cost drops to $21,000.) So over the last two decades the total const of installed residential solar has dropped by about 60%. This is without considering any tax breaks or incentives.

The reason for the decrease is partly that solar panels themselves are cheaper, and they are more efficient, so a 7kW system requires fewer panels. Installation costs have decreased by about 50% over this time, largely due to economies of scale. However, the US still has higher soft costs for solar than many other industrialized nations, and this is mostly a matter of the regulatory system. So there is some regulatory efficiency to be gained. Solar companies themselves also have some possible efficiency gains. Fortunately, the net price for solar continues to go down, with another 50-60% decrease in total price possible even without further technology gains. Commercial and grid scale solar is even cheaper – with large non-residential installations down to $2.4 per watt.

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There is a lot of legitimate concern over the net effects of students using AI to do their school work. If you let AI do your writing, then you will not learn how to write, for example. Or worse – if you let AI think for you, then you will not learn how to think. I have spoken with many teachers at many levels about this and they range from those who take diligent steps to minimize AI “cheating”, to the other end where some throw up their arms and essentially say, “if students us AI to cheat, they are just cheating themselves out of an education.” For me the bigger question is – what happens to society if we raise a generation intellectually crippled by their dependence on AI?

Certainly, the educational system at every level needs to deal with the reality of AI, and needs to protect the legitimacy of their methods for evaluating students. But also, schools need to face the reality of a world with AI and prepare their students for that world, which may require leveraging AI itself as a teaching tool. In fact, using AI as part of the teaching itself may secondarily solve (or at least mitigate) the AI-cheating problem. For example, if you develop a workflow that includes human and AI elements, then use of AI is already baked in and accounted for. Ideally, the outcome will be optimal when the human adds value to the process and uses AI effectively. The outcome should be better than AI alone, or human alone, could generate. And if you have to show your work throughout the workflow, it becomes hard to fake your contribution to the process.

I don’t think researchers were even thinking of the AI-cheating problem when they developed this AI assisted workflow to teach undergraduates how to develop proper research questions. They were trying to more effectively teach students how to develop research questions. But their model may provide a useful template in many other educational contexts, and also help the AI-cheating problem.

Here is the problem they were trying to address – undergraduate students often have a difficult time developing useful research questions. It’s a lot harder than you might think – “Students can usually name a topic they care about, but often lack strategies for turning that interest into a question that is grounded in evidence, scoped to what is feasible, and aligned with available methodologies.” This has long been identified as a bottleneck in undergraduate research education. Developing a solid research question is often the most critical step in any research, and is often the most challenging thing to do.  Continue Reading »

It should be clear by now that drone technology has crossed a technological threshold where they are cheap and efficient enough to have widespread use. They have transformed modern warfare. They are commonly used in surveillance. Over 75% of recent action films use drone footage, which has reduced the cost of aerial footage by about 90%. And now it seems that drone delivery services are poised to expand considerably.

What has caused this advance in technology? Part of it is advances in battery tech, which allows for greater range with lighter batteries, but that is not the whole picture. AI control systems are also critical. Sensor technology has also improved, both in miniaturization and Beyond Visual Line of Sight (BVLOS) technology. This is the ability to communicate with and control drones beyond line of sight using cellular, satellite, and mesh radio networks.

So are we about to see the future as depicted in Ready Player One, with drones constantly buzzing around the sky delivering everything from pizza to VR equipment? Probably not, but we will be seeing more drones. Several companies have been developing their drone delivery technology, and have already made millions of deliveries world wide. Amazon already has drone delivery available in 11 cities with more on the way. Wing is another company with current but limited drone delivery available. Zipline has perhaps the most advance system. They have been using and testing their system is Rwanda to deliver medications and other vital supplies to villages without an adequate road system. They have also now apparently made a deal with Uber Eats to deliver one million meals per day.

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It’s interesting to go back to see articles I wrote years ago to see how they held up. In 2019 I wrote a post about the “Challenges of Deepfakes“, which I just reread. What I discussed held up pretty well – deepfakes have both the effect of spreading misinformation and reducing confidence in real information. I think it’s fair to say that what I and others were warning about at the time is coming to fruition. However, what was interesting was what I did not discuss. ‘

I wrote that article just prior to the LLM revolution, and I didn’t even mention the potential for AI to transform the world of deepfakes. AI certainly makes deepfakes more convincing and harder to detect, especially for video, but that is not really the primary effect. The availability of AI tools incredibly lowers the bar for making deepfakes – that is their real impact. This makes it cheap and technically easy to create them, and this introduces potential uses that would otherwise not be feasible. It’s now possible to flood the zone with fake content, making any attempt at controlling or regulating such content perhaps impossibly difficult.

As I discussed at the time, the existence of deepfakes creates two problems – confusing fake content for real content (Type 1 problem) but also dismissing real content as fake (Type 2 problem). It turns out – the type 2 problem is far bigger, and the ease with which deepfakes can now be created dramatically exacerbates this. It is now a not-unreasonable assumption that any picture or video you might be seeing online is fake. It should be standard procedure to withhold judgement unless and until a video or picture has been vetted and verified. But this is work, and not everyone is going to be willing to do such work, especially for something that supports their narrative.

It is extremely important to note, however, we are not in the alleged post-truth world (not yet) – unless we believe that we are. If you believe you cannot trust any information, then you won’t, and there will not be any objective facts for you. You will be functioning as if we live in a post truth world. But most information online is still accurate, there are still trusted sources, and it is actually not that difficult to spend just a minute or two to verify most pieces of content you may be interested in. This simply has to become part of our information hygiene. And we must resist the temptation to just throw up our hands and conclude it’s all fake. Continue Reading »

When I was a child I would love looking up at the night sky. I became quite familiar with the northern sky, and how it changed from summer to winter. I could clearly see the Milky Way, which was always stunning. I took it for granted.

Today I rarely see the Milky Way. I live in the burbs and can still see a lot of stars at night, but it is nothing like 50 years ago. Even in small cities, you can only see the brightest stars. In order to get an experience like I had regularly in my youth you have to go to a dark sky location – the nearest one to me is a six hour drive. The first two times I visited the southern hemisphere I didn’t see the night sky once. On my third trip I had to make a dedicated trip to a semi-dark sky location to get my first view of the southern sky, which was magnificent.

If all of the currently proposed satellites are eventually launched, there may be no dark skies left anywhere. Ground-based astronomy might also become extremely difficult, to practically impossible. Right now there are over 16,000 satellites in orbit, about two-thirds of which are Starlink. There are current proposals for 1.7 million new satellites. You read that right – over a million. Space X did alter their satellites to make them less bright, which helps, but they are still a problem. Now they plan to launch a million more satellites for orbital data centers. Europe and China have other proposals that would add hundreds of thousands more.

Reflect Orbital is a company that wants to launch giant mirrors into orbit to reflect sunlight down to dark parts of the planet. They hope to launch 50,000 such satellites – each one would be 4 times brighter than the moon if you are  within its reflected light, and as bright as the planet Venus if you are outside that area.

A recent study concludes that if there are over 100,000 satellites in orbit ground based astronomy essentially becomes impossible. Imagine dozens of objects, much brighter than the distant objects you are trying to observe, crisscrossing every image. Essentially, satellites reduce the observing time that observatories have. The more satellites, the less time (as they wait for satellites to clear their view). With 1.7 million satellites (especially the Reflect Orbital ones) ground base astronomy is doomed.  Continue Reading »

I often get questions like the one below:

“I live part time in Japan. Everyone sees hydrogen powered generators, trains, trucks, etc regularly. High density population means it’s easy to get enough synergy to justify the infrastructure. So sad we aren’t doing more to utilize this great tech. Hydrogen combustion engines emerge as cheap alternative to fuel cells.”
https://asia.nikkei.com/business/energy/hydrogen-combustion-engines-emerge-as-cheap-alternative-to-fuel-cells

This is often how news about advances in hydrogen tech are framed. The problem is – the limitations with hydrogen technology have nothing to do with the tech itself, so tech advances are mostly irrelevant. Also, hydrogen combustion is not a better solution for most use cases then hydrogen fuel cells. Fuel cells use an electrochemical process to combine hydrogen and oxygen, producing electricity and water. They are about 60% efficient, produce no pollution, and have no moving parts. Hydrogen combustion operates more like a regular engine, but with hydrogen as the fuel. They are about 40% efficient, produce nitrogen oxides as pollution, and have moving parts that operate at high pressure and temperature. They can be, however, more powerful for heavy applications and are cheaper to build.

For cars hydrogen combustion is a terrible idea. Maybe there is a use case for large generators, trains, and heavy trucks. Even there, however, there are current limitations that the technology of hydrogen engines or fuel cells do not address.

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I have been seeing a lot of news about geothermal energy recently. This is partly because of the Geothermal Research and Development Act – which was passed with bipartisan support. Trump does not like wind and solar, without any rational reason that he can articulate. He mainly supports fossil fuel. Democrats, of course, favor renewable energy and want to phase out fossil fuel as quickly as possible. Republicans seem to be luke warm on nuclear – they support it but are not making massive investments. Democrats historically dislike nuclear, but now have mixed feelings. It’s better than fossil fuel, and we may need it, but renewables are better.

Both sides, however, agree that geothermal is awesome and are all in. Democrats like it because it is renewable – there is no fuel to burn and it is a low carbon source of electricity. Both sides like it because it is potentially a steady and reliable source of large amounts of electricity, with no intermittency problem. Republicans may also like it because geothermal requires drilling and perhaps fracking, which are technologies already used by the fossil fuel industry. They have the technology to drill wells that produce energy, in whatever form. Regardless of the reasons, this is an area of common ground where we can potentially make some progress.

What is the real potential of geothermal energy? Right now geothermal produces 0.4% of US electricity and 0.3% of world electricity. This is fairly insignificant – we  would need a 1-2 order of magnitude increase for this to become a serious player in decarbonizing our energy infrastructure. About 93% of US geothermal comes from California and Nevada, with the rest from Utah, Hawaii, Oregon, Idaho, and New Mexico. This is because of geology – these are locations with recent volcanic activity meaning there is hot rock relatively close to the surface. To understand why this is, let’s quickly review geothermal technology.

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A recent study represents a confluence of technological advances that is pretty interesting. The specific problem being addressed – chemotherapy for bladder cancer – is very promising, but the technology itself can potential have many applications.

Let’s start with the problem, delivering chemotherapy to bladder tumors. The trick with chemotherapy is to create the highest levels of toxicity possible for cancer cells while limiting the toxicity for healthy cells. There are several ways to achieve this, with the primary method being finding drugs that are inherently more toxic tumor cells than non-tumor cells. The classic feature that has been targeted is the fact that tumor cells are rapidly reproducing and have a high metabolic rate, so drugs that target such cells are used. This still has an effect on healthy cells, especially those that rapidly reproduce, like hair cells, which is why many chemotherapies cause hair loss. Essentially we push the toxicity of these drugs to the limit of tolerability, to kill the cancer cells as much as possible.

Another strategy is to get these drugs to the tumor in higher concentrations than the rest of the body. You can accomplish this by intralesional injecting (injecting chemotherapy directly into a tumor), by injecting into the body compartment that contains the cancer, or by directing the chemotherapy specifically to the cancer using advanced techniques (such as lipid nanoparticles). For some types of bladder cancer the strategy has been to fill the bladder with chemotherapy and keep it in there for as long as tolerated. This works to some extent, increasing the dose of chemotherapy to the tumor while limiting systemic effects. However, there is limited penetration of the drug into the tumor because of the nature of the bladder lining.

The researchers therefore developed a hybrid algae robot to delivery chemotherapy for bladder cancer. They started with the silica shell of a diatom algae, specifically Coscinodiscus granii. The shell is called a frustule, which you can think of as a tiny glass wiffle ball. They chose G. granii because it has a multilayered porous shell with different sized pores, including a network of nanopores. They cleaned it of all the living algae parts so just the frustule was left. Essentially they are using the algae as nanofactories, mass producing these finely structured rigid shells that are also biodegradable. Nature so far is much better at creating things at the nanoscale than our technology. They then coated the frustules with magnetite and filled them with a chemotherapeutic drug, doxorubicin.

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In medical education we have a concept called longitudinal care – interns and residents need to follow a case through to its conclusion, or at least long term, so that can learn the entire process. This is why on-call work hours used to be so horrifically long (up to 36 hours), to allow doctors in training to follow an acute case to see the effects of the decisions they make. Training also includes continuity clinics where residents can follow the same patients for months or even years.

I feel that skeptical science communicators serve this role as well – we are the continuity clinics of skepticism. We follow cases through to their conclusion to see what critical thinking lessons we can derive from them. Without this follow up people tend to jump from one extraordinary claim to another, and therefore they don’t close the feedback loop and never update or improve their cognitive methods. In short, they don’t learn the lessons of their past failures, or benefit from the reinforcement of their successes.

Which is why I think it is important to follow up on the claims made earlier this year by Donut Lab. They claimed to have developed a solid state battery with a specific energy of 400 Wh/kg, with over 100,000 charge cycles, able to recharge in 5 minutes, is stable without a fire risk, operates from -30 to 100 degrees Celsius, is cheaper than current Li-ion batteries, and does not require any geopolitically sensitive materials. They presented these claims at CES in January, but notably did not have an actual battery to demonstrate. Their claims were met with pretty universal skepticism. I wrote at the time: “I’m beginning the smell the “fake it til you make it” syndrome that tanked Theranos.”

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