osipov

the only factor that's stopping me from moving to readeck is a lack of import functionality for evernote bookmarks. otherwise, i'd be 100% on readeck.

https://podcastindex.org/apps?appTypes=app

What's your basis for claiming that Tinygrad can't compute 2nd order partial derivatives (i.e. Hessians) needed for LBFGS? Tinygrad like PyTorch uses automatic differentiation which has no problem supporting nth order derivatives.

Commercial pilot here. Instead of climate change, we should be talking about continuous descent profiles (CDPs) that have become more common in the past years 5-10 years. These profiles with idle engines allow for a smoother, more fuel-efficient descent by reducing the need for level-off segments. However, CDPs can increase the perception of turbulence during descent. This is because aircraft remain at higher altitudes for longer periods, where atmospheric instability and wind shear are more pronounced. This increased turbulence is not due to climate change but rather the result of these optimized descent procedures aimed at reducing fuel consumption and minimizing environmental impact.

In a broader economic context, they take profitable but unpredictable companies and make them boring. Google is the more recent example. Apple (pre-return of Job) is another. Here's a good article: https://www.inc.com/justin-bariso/apple-googlemckinsey-how-a...

surprisingly not a word about quantum supremacy

if you don't want pop science articles did you try scholar.google.com instead?

Maybe IBM could have used some of the $110 billion dollars they spent on buying back shares to, I don't know, build a real business?

You just answered your own question. As I pointed out, leadership was "picked" by Wall Street investors. Share buybacks and financial engineering exist to keep the investors happy.

If they didn't buy back shares and invested in R&D the stock would have gone straight down instead of sideways. As I said the leadership "couldn't get the capital to fund internal engineering efforts for the _scale_ of the transformation needed to sustain IBM's success"

ex-IBMer here. I rode this from 2001 until 2016 when it became clear that breaking up the company (as they recently did) would become the only sensible path forward.

While many poor decisions were made inside of the company, I ultimately blame Wall Street for IBM's downfall. Remember that back in 2011, Palmisano finished strong with IBM's Watson winning Jeopardy, IBM Software delivering consistent >80% profit margins on $10Bs in revenue, and a strong services backlog.

Many don't know that Palmisano's departure was preceded by a Wall Street mediated competition for the successor. IBM Software Group SVP, Steve Mills, was the obvious choice. The guy was a lifetime IBMer, intellectually superb, allegedly with photographic memory, effective public speaker, and with a proven history of leading (at the time) the 3rd largest software business in the world.

Unfortunately, Wall Street didn't like Mills because he did not come across well on CNBC. The guy is chubby and doesn't look like a conventional CEO. So Ginny Rometty, with a claim to fame based on building IBM Global Business Services from on the PwC acquistion became the leading candidate. Ginny is "media friendly" and the diversity factor didn't hurt.

Once Ginny came on board, leadership style changed from long-term to fickle and neurotic. Instead of committing to the hard work of building complex technology (e.g. cloud), any signs of technological challenges became reasons for business strategy changes at the top level. What started as a build decision (IBM SmartCloud) turned into a buy decision (Softlayer), followed by a build decision (IBM Bluemix), and so on.

However, Ginny's biggest failure was her inability to raise capital on Wall Street. IBM's engineers weren't failing at building cloud technology because the engineers were terrible (some were, normal distribution rules still apply) but because cost cutting policies starved engineering teams though attrition and lack of hiring. Staffing a team meant bringing in internal hires w/o the right skill set or taking a gamble on offshore (global) resource. At the same time Google was hiring left and right with comparative ease (as an aside, now they are dealing with the consequences).

Bottom line, Ginny couldn't get the capital to fund internal engineering efforts for the scale of the transformation needed to sustain IBM's success with machine learning (Watson) and cloud.

I place the blame on Wall Street since they made the bet on Ginny and then left her out to dry.

IBM was the entire IT industry of 1950s, 1960s, and most of 1970s. It is hard to explain IBM's dominance in today's terms. IBM used to be so dominant that the entire financial industry could not operate without their IBM mainframes and today major banks still run on IBM.

The downside (to IBM) was that Wall Street decided "never again" and fought hard to prevent another company of IBM's scale from reappearing.

The injection is intramuscular, not into the bloodstream.

Recent research (per No Agenda shownotes) showed that unlike traditional vaccines, Moderna mRNA spread through the bloodstream producing and distributing spike protein in the entire body.

There is nothing here but a promise. Back in the day we called this "vaporware".

Linear regression uses a measure of an "error" for every data point. Visually, the error is the vertical difference between a data point and the line/plane of linear regression. In contrast, PCA measures the distance from the data point along the line perpendicular to the PCA axis. The PCA distance is also known as a "projection".

There is something known as orthogonal regression (total least squares) which uses the same measure as PCA. Unfortunately it doesn't work well across incompatible variables.

You can use https://web.archive.org/web/20210104154304/https://www.singl... since the original website is experiencing HN bear hug.

The more perceptive ones at Google have realized that and quit in mass over the past few years. Since Sundar and the rest of the McKinsey gang took over, it has been just death to Google by a thousand cuts.

The post reveals the hackernews bias against discrimination in all forms, regardless of whether it is targeted at minorities or white males. Meritocracy baby!

citation needed

due lack of clear and measurable success criteria

Quora

The analysis of the Game 3 in the article is wrong. You should switch.

Ex-GCP here. Unless you shell out at least $15,000 / mo [1] for a Technical Account Manager (TAM) you are a nobody to Google Cloud. Hence all the bots suspending accounts and scripted processes. If you are a small business, bite the bullet and use AWS.

[1] https://services.google.com/fh/files/misc/enterprise-support...

Use jitsi.org

Swift Playgrounds on a Mac

check out sharpestminds.com

I find it more productive to follow the people of the academia rather than the papers.

Unless you are a researcher (in academia or a corporate research lab), you should think twice before spending your time with these papers.

I have seen repeated examples of information technology industry professionals who go off on a wild goose chase of trying to parse the papers and reproduce them. If you are a machine learning practitioner or a data scientist in the industry, it is highly likely that you are going to waste your time with these papers. Here's a concrete example from the list: "John Lafferty, Andrew McCallum, Fernando C.N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, ICML 2001." This used to be the defining paper in early 2000s. Today it is important only as a road marker in the NLP research history which turned out to lead down an unproductive route.

Those who have not spent meaningful time in academia working on publishing their own research papers tend to fetishize them. The reality is that even the best papers in the field are a mess of ideas designed to please fickle reviewers and academic superiors. Most papers explore nooks and crannies of ideas that are irrelevant to an industry practitioner and are filled with assumptions that turn out to be impractical.

Unfortunately reading research papers has become a self-reinforcing status symbol for practitioners to name-drop and generally show off their in-crowd status rather than to rely on the ideas in the papers for a source of useful and practical information.

It is remarkable that Germans for the most part are unwilling to look for signs of corruption in their government. I recall the conversations that I've had with regular German working stiffs around 2012-2014, chatting about the new Berlin Airport and how that showed symptoms of government corruption. The good folks of Germany started admitting to the possibility of corruption only when the media in Germany began to investigate the situation and uncover problems.

I'm training a machine learning model that honeypots commenters like you with provocative news articles. Next, the comments section is cleaned up to eliminate commenter spam. It works amazingly well!

Shameless plug: check out my book (in preparation) available in ebook format here: bit.ly/sml-book

The launch and the detonation start here: https://youtu.be/nbC7BxXtOlo?t=1309

Clips of nuclear "money shots" are at the end of the documentary: https://youtu.be/nbC7BxXtOlo?t=1779

Makes you wonder how many unreleased "Top Secret" documentaries from the Soviet Union are still on tapes someplace.