The actual paper makes more sense: "the number of galaxies in that field that rotate in the opposite direction relative to the Milky Way galaxy is ∼50 per cent higher than the number of galaxies that rotate in the same direction relative to the Milky Way."
The actual paper makes more sense: "the number of galaxies in that field that rotate in the opposite direction relative to the Milky Way galaxy is ∼50 per cent higher than the number of galaxies that rotate in the same direction relative to the Milky Way."
The actual paper makes more sense: "the number of galaxies in that field that rotate in the opposite direction relative to the Milky Way galaxy is ∼50 per cent higher than the number of galaxies that rotate in the same direction relative to the Milky Way."
Quantitative Strategies and Data Group (QSDG) uses models, data, and analytics to develop and deliver impactful solutions to sales and trading teams across Global Markets. We collaborate across business lines and are guided by the highest standards of governance, ethics and scientific rigor. In your role you will contribute directly to the firm by helping us serve our clients and manage risk. You will be on active projects in the fast-paced environment of the trading floor.
Eligibility
- Hold or predicted to achieve at least 3 A levels/BTEC or equivalent with minimum grades of AAB (136 UCAS Points), with at least an A (or equivalent) in Mathematics
- Have at least a grade A or 8 in Mathematics and at least a grade 6 or B English Language GCSE or equivalent
- Have been a resident of the UK for the past 3 years
- Not in full-time education when starting the apprenticeship
- Have the right to work and remain in the UK indefinitely
- Have not completed a qualification or apprenticeship at the
same level or higher which provided you with substantially the same skills or training as you would gain through this apprenticeship with Bank of America
I think a lot of the hostility about over achieving kids is that parents and their fellow students instinctively recognize that a lot of the success that comes from education is "zero sum".
There are only so many places and scholarships available at the elite universities, employers only pick the academic "winners" for their graduate programs etc.
When a child spends all their spare time studying and does better in life as a result, a component of this success is actually at the expense of the kids who may have been as bright, but did not spend as much of their spare time on it.
So now should all the other kids spend all their time studying so that they can keep up and not lose out? Maybe...
I'm not saying that any of this is wrong or even unfair - just pointing it out.
I read an article that stated that Microsoft lost an anti-trust court case against the EU in which the EU mandated that they allow third party competitors to provide this service. Microsoft has its own solution called Windows Defender.
Not to be that guy but this is the sort of thing I could imagine might benefit from an AI also. Train an AI to recognize drowning, and sound an alarm just in case the lifeguard misses it...
"Students tend to understand objects better" -> I actually a disagree.
A problem is mutability. Consider: "Is-a list of triangles a list of shapes?".
Most inexperienced people, using their intuition, would answer "Yes!", and try to design a "ShapeList" base class of "TriangleList", "CircleList", "SquareList"... and then puzzle over where to put the method "void add(Shape s)".
So you end up picking a subset of OO in which you take away inheritance, or mutability ("state"). But take those away and you don't really have OO any more (IMO)...
Bank of America | London, UK | Full-time | ONSITE |
We are seeking a highly skilled and innovative strategist / technologist to join our dynamic cross asset strategy team. As a key member of the team you will be responsible for developing and implementing advanced trading strategies and responding to end-user requests. Our team writes programs in Python that run on the Bank’s strategic platform, Quartz, to answer questions that relate to the entire global markets trading business. We work outside of the traditional IT organisation, based on the trading floor in London.
You will be assisting the Cross Assets Strats team as part of the Strategic Risk and PnL project to re-factor and redesign our market model code.
This project seeks to explain the risk and PnL of the Global Markets business in a strategic fashion.
Working as part of a team of 7 people, you will be writing and debugging code in Python, running within Quartz, the in-house bank platform.
On-going learning about and debugging existing market model code used for calculating risk and PnL, simplify and optimize to perform more efficiently.
Programming ability is most highly prized. Financial knowledge is desirable but not strictly necessary, provided there is a willingness to learn.
Please get in touch if:
If you are passionate about programming and are comfortable with a variety of programming languages and paradigms
* You have previously written scripting language, or at least know how you might go about it
* You appreciate functional programming concepts and writing algorithms in a functional style
* You have good mathematical abilities and want to learn more about using mathematical techniques to analyse data
* You have financial and quantitative knowledge, and would like to learn more
Key Requirements
* UK resident - Unfortunately, for this role, we are not able to consider applications from candidates who are not already resident in the UK.
* Bachelors or master’s degree in computer science, Mathematics, Finance or related field
* Experience within quantitative trading, algo trading or related role
* Proficient in Python, C++, C#, Java or Lisp (etc.!)
I feel like the whole article is an allegory of LLM development (as a critic would write it: devoting a stupendous amount of resources and 'training data' to 'memorize' the solution). I wonder if this was the author's intent?
yes and perhaps the difficulty of using a frequentist approach on a sample size of 1 is why the parent post thought that you cannot learn anything from a sample size of 1...
I think this is too pessimistic. One always gains some information, even from a single event. If you're catching the bus one and shows up literally the minute that you arrive at the bus stop, yes, you may have been very fortunate and in fact the bus runs only once per day, but you really ought to assign a lower probability to that possibility now, given your recent single new piece of information. There are ways to express this quantitatively too - but your own intuition ought to be enough of a guide here.
This. I remember forking out... $50?... to buy the Commodore 64 Reference Manual from a specialist bookstore (we had to drive there). I was so excited when I brought it home and could finally write programs in assembly (to put sprites on the border using raster interrupts!).
We require a strat/technologist to work in the Cross Asset Strats team. We work outside of the traditional IT organisation and sit on the trading floor in London, responding directly to end-user requests.
Our team writes programs in Python that run on the Bank’s strategic platform, Quartz, to answer questions that relate to the entire global markets trading business.
Please get in touch if any of the following apply to you:
* You love to program and are comfortable with a variety of programming languages and paradigms (e.g. Lisp, Haskell, C++, Python).
* You have written your own scripting language, or at least know how you might go about it
* You appreciate functional programming concepts and writing algorithms in a functional style
* You have good mathematical abilities and want to learn more about using mathematical techniques to analyse data
* You have financial and quantitative knowledge, and would like to learn more
- the "inner product" operation (integral of the product of the two functions): imagine what would happen as you approximate functions as discrete vector with a very high number of dimensions/co-ordinates and computed the dot-product between those two vectors, but scale the result to be invariant of how many dimensions you used to approximate it => you get the integral formula
- Now, it's just normal linear algebra:
- The "length" of one of these vectors can now be thought of as the square root of the inner product of the function with itself
- The "distance" between two functions can now be thought of by subtracting one function from the other, to get a new "vector/function", and compute its length
- The cosine of the "angle" between two functions is the dot product between two functions scaled to have length 1
- The functions describing a sine or cosine wave are vectors which have a inner-product against themselves of 1, and a dot-product against any other frequencies of 0
- Thus the different frequency functions/vectors form an orthonormal basis
- This means that you can find the co-ordinates of any function by taking the inner product of the function against each fourier basis function
- The "co-ordinates" of your function w.r.t. the orthonormal basis can be computed by taking the inner product against each basis function/vector
- This will be the point that minimizes the distance to your actual function
- These "co-ordinates" are the fourier co-efficients for the fourier series representation of your function
- For non-periodic functions, you can take the limit as your period goes to infinity, that gives you the fourier transform representation.
ah and I get it now - the author has associations with the alt-right. For those who had heard of him (I had not), they were already inclined to view his statements uncharitably, even though I think this particular essay does not have any real "alt-right" themes.