Aleksandr Kharkhota

🇪🇸Python/Scratch programming languages teacher. Doing Master's in Data Science.
#FPL player.

[Aleksandr Kharkhota @python_teacher]

@MarkkuOjala_ @AlexVolloFPL 🤣👍

[Aleksandr Kharkhota @python_teacher]

@AlexVolloFPL Thought he couldn't shoot properly with his left foot) thnx

[Aleksandr Kharkhota @python_teacher]

@markrstats Why there's 2 Semenyos on the chart?

[Aleksandr Kharkhota @python_teacher]

@AlexVolloFPL Was in the same boat. Got much more joy to actually play it (FPL) as a game, to make risky moves even if it doesn't work out! Thanks for your position mate

[Aleksandr Kharkhota @python_teacher]

@AlexVolloFPL Sure. Pls run!

[Aleksandr Kharkhota @python_teacher]

@AlexVolloFPL For sure!

[Andrej Karpathy @karpathy]

nanoGPT - the first LLM to train and inference in space 🥹. It begins.

[Aleksandr Kharkhota @python_teacher]

@AlexVolloFPL Is it some data provider on which this DEFCONs data is based? Cannot find any mention

[Andrej Karpathy @karpathy]

Sharing an interesting recent conversation on AI's impact on the economy.

AI has been compared to various historical precedents: electricity, industrial revolution, etc., I think the strongest analogy is that of AI as a new computing paradigm (Software 2.0) because both are fundamentally about the automation of digital information processing.

If you were to forecast the impact of computing on the job market in ~1980s, the most predictive feature of a task/job you'd look at is to what extent the algorithm of it is fixed, i.e. are you just mechanically transforming information according to rote, easy to specify rules (e.g. typing, bookkeeping, human calculators, etc.)? Back then, this was the class of programs that the computing capability of that era allowed us to write (by hand, manually).

With AI now, we are able to write new programs that we could never hope to write by hand before. We do it by specifying objectives (e.g. classification accuracy, reward functions), and we search the program space via gradient descent to find neural networks that work well against that objective. This is my Software 2.0 blog post from a while ago. In this new programming paradigm then, the new most predictive feature to look at is verifiability. If a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well. It's about to what extent an AI can "practice" something. The environment has to be resettable (you can start a new attempt), efficient (a lot attempts can be made), and rewardable (there is some automated process to reward any specific attempt that was made).

The more a task/job is verifiable, the more amenable it is to automation in the new programming paradigm. If it is not verifiable, it has to fall out from neural net magic of generalization fingers crossed, or via weaker means like imitation. This is what's driving the "jagged" frontier of progress in LLMs. Tasks that are verifiable progress rapidly, including possibly beyond the ability of top experts (e.g. math, code, amount of time spent watching videos, anything that looks like puzzles with correct answers), while many others lag by comparison (creative, strategic, tasks that combine real-world knowledge, state, context and common sense).

Software 1.0 easily automates what you can specify.
Software 2.0 easily automates what you can verify.

[Andrej Karpathy @karpathy]

I took delivery of a beautiful new shiny HW4 Tesla Model X today, so I immediately took it out for an FSD test drive, a bit like I used to do almost daily for 5 years. Basically... I'm amazed - it drives really, really well, smooth, confident, noticeably better than what I'm used to on HW3 (my previous car) and eons ahead of the version I remember driving up highway 280 on my first day at Tesla ~9 years ago, where I had to intervene every time the road mildly curved or sloped. (note this is v13, my car hasn't been offered the latest v14 yet)

On the highway, I felt like a passenger in some super high tech Maglev train pod - the car is locked in the center of the lane while I'm looking out from Model X's higher vantage point and its panoramic front window, listening to the (incredible) sound system, or chatting with Grok. On city streets, the car casually handled a number of tricky scenarios that I remember losing sleep over just a few years ago. It negotiated incoming cars in tight lanes, it gracefully went around construction and temporarily in-lane stationary cars, it correctly timed tricky left turns with incoming traffic from both sides, it gracefully gave way to the car that went out of order in the 4-way stop sign, it found a way to squeeze into a bumper to bumper traffic to make its turn, it overtook the bus that was loading passengers but still stopped for the stop sign that was blocked by the bus, and at the end of the route it circled around a parking lot, found a spot and... parked. Basically a flawless drive.

For context, I'm used to going out for a brief test drive around the neighborhood to return with 20 clips of things that could be improved. It's new for me to do just that and exactly like I used to, but come back with nothing. Perfect drive, no notes. I expect there's still more work for the team in the long march of 9s, but it's just so cool to see that we're beyond finding issues on any individual ~1 hour drive around the neighborhood, you actually have to go to the fleet and mine them. Back then, I processed the incredible promise of vehicle autonomy at scale (in the fully scaleable, vision only, end-to-end Tesla way) only intellectually, but now it is possible to feel it intuitively too if you just go out for a drive. Wait, of course surround video stream at 60Hz processed by a fully dedicated "driving brain" neural net will work, and it will be so much better and safer than a human driver. Did anyone else think otherwise?

I also watched @aelluswamy 's new ICCV25 talk last week (https://t.co/RdaM23kvez) that hints at some of the recent under the hood technical components driving this progress. Sensor streams (videos, maps, kinematics, audio, ...) over long contexts (e.g. ~30 seconds) go into a big neural net, steering/acceleration comes out, optionally with visualization auxiliary data. This is the dream of the complete Software 1.0 -> Software 2.0 re-write that scales fully with data streaming from millions of cars in the fleet and the compute capacity of your chip, not some engineer's clever new DoubleParkedCarHandler C++ abstraction with undefined test-time characteristics of memory and runtime. There's a lot more hints in the video on where things are going with the emerging "robotics+AI at scale stack". World reconstructors, world simulators "dreaming" dynamics, RL, all of these components general, foundational, neural net based, how the car is really just one kind of robot... are people getting this yet?

Huge congrats to the team - you're building magic objects of the future, you rock! And I love my car <3.

[UEFA Champions League @ChampionsLeague]

If you haven't seen Micky van de Ven's solo goal yet watch this! 😱

...and if you have seen Micky van de Ven's solo goal watch it again! 🤯

#UCLGOTD | @Heineken

[Aleksandr Kharkhota @python_teacher]

@FPLBenchwarmers Got it, thanks for clarification!

[Aleksandr Kharkhota @python_teacher]

@FPLPingreen @dis_fpl Welcome!

[Aleksandr Kharkhota @python_teacher]

@LiveFPLnet All this stuff feels like some "interactive coding problem" from some competition 😂

[Aleksandr Kharkhota @python_teacher]

@SpirosValouxis Everyone speaks in my surrounding actually. Pep invited Pep Lijnders and eventually bought players like Donarumma which is a clear signal - they're changing the play style

[Aleksandr Kharkhota @python_teacher]

@elevenify That's a very nice comment. Personally I think Arteta is doing a great job.

[Aleksandr Kharkhota @python_teacher]

@SpirosValouxis How is the decision about the chips made by the model? What are the key factors?