educators-from-the-united-kingdom

"And so what you want is the capability to both do large batch training very efficiently, but also to single-batch inference very efficiently. And so we can do that with the SambaNova systems."

"The idea of AI replacing politicians is far-fetched, but AI can certainly influence how politics work."

"I’m deeply concerned about the political response to climate change. The failure to address it is one of the most pressing issues facing humanity today. Politicians often avoid making difficult decisions, choosing short-term gains over long-term solutions. I think we need leaders who will make the tough choices, like former President Obama tried to do, but unfortunately, this is a challenge that’s far from being solved."

"AI’s potential to replace jobs is one of the biggest societal challenges. Just like how the industrial revolution reduced the need for physical labor in factories, AI and automation are making certain jobs obsolete. This raises crucial questions about the future of work and how society will adapt. What will humans do when robots can perform tasks like driving, cleaning, or even teaching? It’s a problem that requires long-term thinking, especially since automation leads to greater wealth concentration in the hands of a few."

"Right now, AI is still just a very advanced tool. It doesn’t have the ability to make its own decisions or have its own goals like humans do. To be human, you need to have agency, a purpose, and a soul. AI hasn’t reached that level yet. Maybe in the far future, it’s possible, but it’s still a long way off—probably around 50 years."

"I think it’s a great idea to develop AI talent outside of traditional hubs like the Bay Area. Personally, I’m from Nigeria, so I see the value in growing research and innovation in regions that might not be the typical focus."

"I am a self-made entrepreneur. I started off as a small-time writer, now I own publishing and IT companies. I do what the hell I want to do."

"So think of SambaNova systems as this capability of doing training and inference very efficiently. And then the real full circle: Once you can do training and inference on the same platform, you can dynamically switch between them."

"And again, you want to be able to handle those large embedding tables because the larger the embedding table, the more accurate the model, the better recommendation you’re able to give."

"The whole goal is, of course, to provide the capabilities to be able to train very, very large, accurate models. If you look at the current landscape of computing capabilities, mostly dominated by GPUs, what you need is many, many GPUs because of the limited amount of memory that each of the GPUs can have."

"What SambaNova brings to the table is the ability one or two or a quarter rack of capability to be able to provide terabytes of memory. And so that allows you to build huge models that can serve any of the particular industrial verticals or commercial verticals that are of interest."

"For instance, huge natural language processing models for the financial sector or for developing chatbots and customer services, voice-based commerce. Also, natural language processing, finds use in cancer research also."

"Or huge vision models that we call true resolution that allow you to do medical images without reducing the resolution to make the image more blurry, so that you can fit it into the memory requirements of conventional systems."

"I’ve always enjoyed working in a multidisciplinary context and very much in alignment with patients and their families."

"Up until a couple of decades ago probably, sickle cell was very marginalised and quite neglected in terms of its status, if you like, within the hierarchy of illnesses."

"That has changed. There’s still work to be done, of course, but I’m delighted that nurses have played their role along with other professionals and families to ensure that the disease is fully understood, and treatment is available across the country."

"I’m one of the patrons of The Sickle Cell Society, the national charity, so I am constantly aware that there are still areas that need to be improved, where there’s been, sadly, for example, a couple of deaths that shouldn’t have happened."

"My moment where I connected with science was strangely through an acronym. From memory, the acronym, MRS NERG (Movement, Reproduction, Sensitivity, Nutrition, Excretion, Respiration, Growth), helped me learn what made something a living organism, and from there I didn’t look back!"

"transitioning from academia to the pharmaceutical industry was difficult. I didn’t have any industry experience before applying, and while I knew I had technical and transferrable skills to be an asset to many companies, it took a monumental amount of effort and interviews to convince anyone to give me a chance. Adjusting my CV so that it was more tailored towards industry rather than academia was crucial during this stage."

"There are so many great parts to the job, but one of the best parts is getting to learn about different diseases, and then be involved in developing antibodies that could one day become medicines to fight these diseases."

"If you’re on the fence about a career in the sciences, my biggest piece of advice is you won’t know unless you try. If there are opportunities to find out more about what STEM professionals do, go for it! It may help you decide if a career in STEM is right for you."

"I sensed that this was a kind, caring woman who wanted to avoid hurting me. And then I discovered she was something called a nurse and I thought, nurse, I like the idea of that."

"That’s when I decided I wanted to be a nurse and I never changed my mind at all."

"During that time, I retained links with community nursing, but also with acute nursing by having a clinical link in an NHS Trust on a ward involved with the care of patients with sickle cell disease."

"Because it was an innovative position, seen as pioneering at the time, I could actually develop quite a lot of it in the way that fitted my ideals of multidisciplinary activity."

"What was the first application of numbers? ...[W]e're pretty sure that the first application... was ...the tax man. Why can we be sure... because if you go to museums... you can find Babylonian cuneiform tablets... and the [Egyptian] Rhind papyrus... where there's loads of wonderful maths developed, and it's all to help the tax man."

"But they then discovered something else, and... had to extend numbers a bit more. Suppose a farmer has a field and that field grows 100 cabbages. ...[T]he king wants to have a war so they need... 200 cabbages. How much bigger should my field be? ...It needs to have twice the area, and the area... is proportional to the square of the length of the field, so... how much bigger should the length of the field be, and the equation that you have to solve... is[W]e know the ns were interested in this problem because... a cuneiform tablet, which I believe is in the ... is... trying to solve this equation.... and... gives the answer. ...[T]hey tried to solve this using fractions and they... couldn't. There was no fraction which equaled the answer... and so they had to invent... what we call an irrational number to give a solution... 1.4142135623730950488... That's to 20 decimal places, and it goes on and on and on. ...[T]hese were numbers called s, and were originally invented for the tax man to work out how to double the area of fields."

"The great triumph of maths around... 1690 was the development of calculus... and calculus is now probably the best tool that we have in math to tackle the problems of the real world."

"We had a... newspaper competition in the U.K.... to identify the greatest ever invention... and I wrote in ...calculus. ...It didn't win. ...The greatest ever invention was apparently the ...the second was the , and the third was fire... which was misguided because calculus is, without a doubt, the best tool that we have... But of course, I am biased."

"[A] lot of maths doesn't develop by solving problems of practical importance. A lot of it... develops purely out of curiosity, of from doing stuff for fun! ...You're doing maths when you do Sudoku, and it's good fun ...[S]olving puzzles ...and having fun is ...an extremely good way of doing math, probably the best way."

"[[w:Recreational mathematics|[R]ecreational math]] is a huge... subject... [A] particular favorite of mine... s and labyrinths, which were originally recreational. One of the earliest examples... involves... the ... the product of a between the queen of King and... Zeus, dressed up as a bull... turned into a bull, or whatever they do. The product... was the Minotaur... half man and half bull... [H]e was... ferocious and... lived in the center of a labyrinth underneath the palace of King Minos. ...Theseus... said I will go with the 9 young men and 9 young women and... attempt to kill the Minotaur. So when he went to Crete he was met by one of my heroes... ... the first female mathematician... recorded in the classical literature. ...[S]he gave him a sword. By the way she fell in love with him. ...[S]he also said ..."I will give you an algorithm for cracking the labyrinth ...and using this algorithm, he went into the labyrinth, found ...[and] killed the Minotaur, got out of the labyrinth and took the young men and... women back to Greece... [O]n the way he stopped off at an island... where they had a great party... and only after they had sailed off did they realize they'd left Ariadne behind, and she died of a broken heart and turned into a spider... [T]he real hero of the story is the labyrinth. ...[T]his design, although they've found it everywhere in the ancient culture, is universal. There are clear examples of Native American populations in the U.S. having essentially discovered the same design."

"Maths is universal."

"[I]n the 18th century the idea behind the labyrinth was evolved into... the modern maze, and people... used to build mazes in their large houses... designed to trap the unwary. You'd go into them and... occasionally get lost... People would try to puzzle how to get from the entrance into the center."

"Euler... worked out the math of how you could get into the center and... back out again, and that math led him into the theory... of networks, and that all came out of mazes. What do we use networks for now? ...[T]he biggest network in the world is the internet ...and Euler's work on mazes is directly used to help us do the internet. ...[W]hat uses that, Google. Understanding networks, combined with Matrix Theory (due to Cayley) also forms a major part of the algorithms behind Google."

"I want to tell you a little... about some of the algorithms that have been used to solve mazes. ... ...was a mathematician and a computer scientist, because she came up with [an] algorithm for solving the maze or the labyrinth, and her algorithm was beautifully simple, but remarkably effective. She gave Theseus a ball of thread... and as you go into the labyrinth you unwind it and... to get out, you wind it up again... It worked very well, and that is the basis of a modern computer algorithm called the Flood algorithm for solving the labyrinth."

"Another algorithm is, if you... read the book '... they try and solve , and... Harris... says you solve it by always turning left, or... you put your left hand on the hedge and keep it there, and that will actually work... and it will solve a lot of mazes. It won't solve all of them, but it's... a very good algorithm to try. It will always get you out of a maze, even if it won't get you into the center. So always turning left is a good algorithm."

"A labyrinth with this of [King Minos] design is... unicursal, which means that you can go in and out without making any decisions... So you didn't need the thread after all, but the other mazes like Hampton Court and , and all the other ones, you have to do a little... more. So a labyrith is something where you don't have to make decisions..."

"People often say there's a close link between math and music, and mathematicians and musicians, and that's absolutely true. Music uses a lot of math. So some musical notes sound better when you play them together. ...The reason was discovered by... Pythagoras. ...He is very very famous for Pythagoras' theorem, which was invented by the Chinese about 1,000 years before him. ...But he absolutely did do the work on musical notes. ...[H]e measured the length of strings of instruments and he compared the lengths with notes that sounded good together. ...He realized that the octave [C:C] corresponds to two strings, one being twice the length of the other [2:1], C:G ... 3/2 and C:E proportion 5/4, and Pythagoras found an incredible link between musical harmony and fractions."

"Pythagoras... took the idea further. He said... let's suppose that we have a sequence of notes with simple fractional relationships. So he came up with notes with these relationships [the Just scale] [C] 1 : [D] 9/8 : [E] 5/4 : [F] 4/3 : [G] 3/2 : [A] 5/3 : [B] 1/ 5/8 : [C] 2 So Pythagoras invented the scale... the basis of modern Western music..."

"This scale was used... up to about the 18th century... [when] keyboards were invented. ...It was found that this scale worked brilliantly for one key, but terribly in another key... because the ratios differ... and so the mathematicians developed a different [[w:Equal temperament|[well tempered] scale]] where the notes were constantly varying in frequency [in the same proportion, a geometric progression of the semi-tone frequencies], so the ratio is this wonderful number 1.059.. = 2^\frac{1}{12} which is the number when multiplied by itself 12 times gives 2, which works well in all keys."

"Bach was very familiar with this work going on, and he so liked the well tempered scale that he wrote... ' where you go through every key on the harpsichord... and it's all based on maths, and it's the maths of s... [I]t all goes back to mathematicians working with musicians. We're not evil, souless people at all."

"In the 18th century... people traveled... by boat, but the problem... was... that they were finding it very difficult to know where they were. ...[O]ne of the big problems of the 18th century was finding your position at sea. ...We have , which is the angle from the equator, and , which is the angle measured round... from London. ...I've stood on the zero longitude line in . ...[T]he first thing they cracked was latitude. So you could work out your latitude... by using... the , which measures angles very accurately... [T]hey realized that if you measured the angle between the sun and the horizon, or between the pole star and the horizon, you could... accurately find your latitude. ...[Y]ou had to use a ton of ... [i.e.,] more math, in fact trigonometry on the surface of a sphere, which is... ... [T]rigonometry and the sextant combined... meant that navigators could work out their latitude... how far above the equator they were."

"So one way to get to America was that you would sail from England on the same latitude, and when you got to something big that was probably America, or possibly Canada."

"was the big problem... Many mathematicians tried to solve it using math alone, but the solution was a beautiful one, and it's the sort of math I do... a combination of sums on paper, but also a lot of work with a computer, so it's combining technology with formulae. ...[T]he solution ...was due to ...Harrison, who was a clockmaker... [I]t was based on the observation that the earth goes once around every 24 hours and so if you can time when the noonday sun is, and measure that time on a clock which is the same as the clock in Greenwich, for example, then by measuring that time you could... work out how far around the earth you are. You have to do a whole ton of other stuff as well, but that's the basic idea. ...[T]hat was math, but it was also linked up with technology because you needed the clock ...[H]arrison's clock, called H4 ...was the first clock ...accurate enough to make that possible."

"[A] ton of math was involved as well. You have to produce tables of the motions of the sun and the stars... These ephemerides... were calculated by computers. ...A computer in those days was a room full of people who computed. ...They were typically youngish people, often students, and it was found ...that women were better at it than men ...[T]he midshipmen on the boat doing navigation had to do long [tedious 22-step mathematical] calculation based on these [tables] to [plot a ship's position] find where they were. But those calculations changed the world."

"Why is America such a powerful nation? Because you could reliably sail your ships to Europe and sell all your stuff, and that reliability came from navigation, and that navigation relied on math."

"The calculations were tedious. They took a long time. They were also very error prone, and so people thought... can we automate these using machines? So... Babbage, working with Ada Lovelace... a very very fine woman computer programmer, essentially the first ever computer programmer, worked together to design the machines which would automatically calculate these tables [the Ephemerides.] Sadly they never managed to build them properly due to engineering problems, but Babbage's machine has been recreated... at Science Museum. You turn the handle, all the cog wheels go round and it calculates the tables for you. Brilliant! ...[B]abbage's ideas led directly on to the invention of the modern computer, with Turing, von Neumann and people like that, and that all came directly by navigation and the need to do that."

"I would argue that mathematicians save hundreds, if not thousands, if not millions of lives, almost every day. ...Medical scanners have revolutionized medicine because you can be scanned and... find out what's wrong... without cutting you open. ...[T]he medical scanner was basically invented by ...Radon ...one of my favorite mathematicians ...[H]e did brilliant maths which is used in medical imaging and saves millions of lives. He's a fantastic mathematician, and he was studying in 1917 a kind of abstract problem. He was looking at shadows. You know, objects cast shadows, and he wanted to know if you know what the shadows were, can you find out what the object was that cast them... [H]e wrote down a formula for taking an object and the shadows it cast, and then with a bit of genius, he worked out another formula saying, if you know what the shadows are, this is what the object is. f is the object, R is the object.{{center|1=Shadow \quad R(\rho,\theta) = \int f(\rho \cos(\theta) - s\sin(\theta),\rho\sin(\theta) + s\cos(\theta))ds Object \quad f(x,y) = \frac{1}{(2\pi)^2} \int\limits_{-\infty}^{\infty} \int\limits_{0}^{\pi} \int\limits_{-\infty}^{\infty} e^{ik(x\cos(\theta)+y\sin(\theta)-\rho}) R(\rho,\theta) \left\vert k \right\vert dk d\theta d\rho}}"

"and are basically the same maths as scanning. [If you solve Killer sudoku you are using the math of .]"

"This was the first photo of Saturn that was taken by a satellite. It was taken in the early 70s by the Voyager satellite. That was... beamed to earth by a transmitter with 30 watts of power... less than that light bulb. That light bulb, well you can see it, but imagine from Saturn, which is millions of miles away... [T]hey took this picture, turned it into numbers and... turned those numbers into a code, and that code had lots of [redundant] information put into it, so it didn't matter how far it went and how much it was distorted. You could reassemble it at the other end, into the picture... ...[I]t's absolutely wonderful. It's a combination of math, and technology and brilliance."