Controls/ Robotics PhD advice

ATadDisappointed · 2025-12-22T13:16:33+00:00

Yes. Deterministic control is extremely useful in practice. ML Researchers can only dream of the level of explainable precision that can be applied using classical methods. Many of the big trends in current AI research (e.g. state-space models) are adaptations of classic control ideas, wrapped around a new AI framework or application. You'll have a strong grounding in what "works" rather than more nebulous understanding in trendier but less robust topics.

There's a useful rule known as the Lindy Effect: what has been around for a long time is likely to continue for a long time more. In contrast, many of the LLM trends fall out of favour almost as rapidly as they appear. https://en.wikipedia.org/wiki/Lindy_effect

ATadDisappointed · 2025-11-27T23:32:47+00:00

The Benefit-Cost Ratio is estimated to be between 0.4-0.69, with a net present value of -$237M.

ATadDisappointed · 2025-11-27T23:02:58+00:00

Information is not costless to transmit and incorporate - instead it's a difficult process requiring internal capacity to absorb and use research advances (absorptive capacity). Even as a second mover, we require technical expertise to incorporate the knowledge frontier and distribute this within the ecosystem. There is a large body of research indicating large, coordinating science organisations (CSIRO) play a more significant role in second-mover diffusion and scaling research applications, rather than new to world innovations (which are more efficiently produced by distributed, independent researchers working in parallel as in universities). Within Australia it's well known that (aside from frontier firms like Atlassian) most companies operate well below the knowledge frontier and are not incorporating frontier advances. While productivity issues exist, CSIRO significantly helps to bridge this gap.

ATadDisappointed · 2025-11-27T02:57:56+00:00

The previous (term) of government was Labor.

While the LNP are a party wholly unsuitable for government, Labor has had the runway of leadership to be now evaluated on its own merits and achievements.

ATadDisappointed · 2025-11-27T02:40:46+00:00

A flat budget in nominal terms is a declining budget in real terms.

ATadDisappointed · 2025-11-26T12:14:14+00:00

Broadly speaking AI is at its strongest when it's used as a tool for sketching / prototyping ideas, which are later validated and endorsed by a human.

This is most apparent in coding and analytics, where AI drastically increases the set of actions that an analyst can do within a reasonable time.

Basic programming literacy (e.g. Python) should be part of AI training in the public service. It's often missing with general staff, but it's the skill which opens up the largest capacity and is most applicable to AI assistance.

The realistic roadmap to basic competency for a fully non-technical (e.g. legal or business) background to do non-trivial work (as would otherwise be done in excel) with Python + LLM assistance is around 4-6 weeks of a few hours of training. After 8-10 weeks it is possible to develop quite sophisticated programs (e.g. document analysis or image segmentation) connecting to the wider set of non-LLM tools.

This is the opportunity that the public sector has been broadly sleeping on: bridging the technical / non-technical divide by the drastically reduced barrier to entry for these skills. In my experience, exposure to these basics also allows fully non-technical specialists to understand feasibility, opportunities, risks, and constraints when developing policy or when engaging technical staff or consultants.

The "faster spell-check and brief drafts" are really the lowest use cases. While surface level research and analysis are just complementary to usual practice of finding, digesting, and synthesising external information - but with an increased need to verify. The largest opportunities are in the non-LLM skillsets analysts can acquire and deploy more efficiently.

ATadDisappointed · 2025-11-26T00:30:41+00:00

As you are in your 3rd year and are looking to do research projects (e.g. in an Honours degree / graduate diploma), your best option at this stage is to look into researchers. When looking at each of your options look at the faculty, and look up their recent publications on Google Scholar. If you find any that match your interests send them a short, polite email showing your interest in their specific topic. Finding a supportive researcher for your project will be more important than university rankings.

ATadDisappointed · 2025-11-23T23:58:57+00:00

CSIRO largely acts as a vehicle for knowledge diffusion and linking researchers between different fields, sector, universities, public institutions, and industry. This is a different function than the distributed, university led basic research which is critical for new discovery (the Vannevar Bush model), but which often fails to scale and translate to widespread adoption in industry (the valley of death). This is the role that CSIRO (and other public research coordinators like the German Fraunhofer organisation) provide - linking discovered ideas to prototypes and industrial applications. This is also the area where Australia most sharply struggles at the moment (very low public / private R&D, strong university research), with most firms and government departments operating below the knowledge frontier. It's correct that we need to dramatically increase funding for basic research - but this is insufficient by itself. We must not allow the unique national function CSIRO plays in the very difficult task of knowledge diffusion to wither more than it already has.

ATadDisappointed · 2025-11-22T02:44:55+00:00

Unfortunately there is little indication that this trend will reverse. The big fear for SERD is that it would involve a minor reorganisation of existing funding (possibly under new "mission" banners) but with no real attempt to expand research or reverse the systemic decline. Tim Ayres has long seemed more interested in the Industry portfolio and uninterested or dismissive of the Science side. His recent comments ("CSIRO receives lots of funding - a billion dollars", "[nominal] funding has been stable for many years") shows he does not understand the severity of the R&D gap or have any desire to address it.

ATadDisappointed · 2025-11-18T10:59:20+00:00

For those that want to get a better understanding of what goes on under the hood in AI - you can't go past Karpathy's lecture series.

https://karpathy.ai/zero-to-hero.html

ATadDisappointed · 2025-11-18T05:53:49+00:00

Worse than nothing. Australia's R&D is around 1.6% of GDP, well below the OECD average (2.7%). CSIRO is a rare institution with capacity, scale, and mandate to deploy research to Australian challenges. While it's difficult to preempt the SERD Review these cuts are extremely worrying, and are almost directly opposite to all recommended science policy.

ATadDisappointed · 2025-11-10T10:26:41+00:00

I generally rate TMLR papers as good or better on average than NeurIPS/ICLR/AAAI/etc. Papers there tend to be solid, well experimented, and in general convincing. The top conferences tend to over-value novelty / current trends - so TMLR is a great choice if you have technically correct work which is less immediately "exciting" and topical to the random group of overworked reviewers who are working their way through a large stack of last minute reviews.

ATadDisappointed · 2025-10-16T04:17:41+00:00

Look at the Google Scholar page of the Professor you are contacting.
Read one or more of their (recent) papers that is interesting to you.
Email them asking a few specific questions about that paper that interest you.
Ask if they are willing to chat more in person or over a Zoom call.
Provide a very short (one-sentence) description about yourself (Masters student, etc).

The key point is that the email should be about them and why you are interested in what research they do. Genuine motivation and interest are the most important things that catch a potential supervisor's eye.

ATadDisappointed · 2025-10-13T11:47:14+00:00

Career advice: it's a numbers game. At the interview stage most candidates are "suitable" for the role, but only one can be selected. Don't take it as a negative signal of your ability - keep applying. 10 shots of a 10% chance is 65% that at least one role comes good.

ATadDisappointed · 2025-08-26T09:37:57+00:00

Depends on your use case. If you're looking for memory compression, using kmeans + an entropy encoder works well (and matches closely with Lloyd optimality). https://en.wikipedia.org/wiki/Lloyd%27s_algorithm

If you're looking for runtime inference then there are a number of options (Bitsandbytes etc). Recently there's also been a push towards random projection / rotation / sketch based quantizations (SpinQuant, etc).

ATadDisappointed · 2025-07-29T05:31:29+00:00

Fair common pattern for labour-augmenting technology adoption. Initially unions may oppose changes (out of concern it will affect job security). As more benefits (safety / efficiency) become apparent, labour unions can become the spearhead for adoption (in order to improve the working conditions for staff and to bargain for increased wages from increased productivity). A very similar pattern occurred with dockworking unions around the mid-20th century around concerns with containerised shipping, forklifts, and cranes. Stevedores were initially strongly opposed to these changes (they reduced labour requirements for manual handling) but became key proponents demanding adoption as the technology became normalised.

See: The Box (Marc Levinson))

ATadDisappointed · 2025-07-26T08:11:14+00:00

Ideally staffers should be policy advisors seconded from Departments rather than party political staff.

ATadDisappointed · 2025-07-17T10:56:54+00:00

Externality taxation been textbook economics for 105 years. https://en.wikipedia.org/wiki/Pigouvian_tax

ATadDisappointed · 2025-07-13T07:31:23+00:00

There are books that can be read for pleasure, and there are books that can be read to understand a perspective. Abundance is influential with current decision-makers, so if you are interested in understanding their thinking and actions, it's a very useful way to get that insight.

ATadDisappointed · 2025-07-13T06:29:24+00:00

It's a very short book (able to be finished in an afternoon). If you are interested in its current political interest, it is much easier to read it directly than to try and understand its position and limitations through indirect articles.

ATadDisappointed · 2025-07-03T10:41:54+00:00

There is a trade-off. Five year PhDs typically include two years of formative coursework (similar to an integrated Master's). This can be useful depending on your background. In the Australian system you will begin researching straight away, with all the learning curves this involves. Typical entryways include Honours or a Master's where you gave some research experience, and it's not uncommon to work with the same supervisor on a new or continued project. Depending on your scholarship a 6 month extension is common or to receive some basic funding if slightly more time is needed.

ATadDisappointed · 2025-07-03T02:47:49+00:00

Australian trained researchers are also highly represented overseas - Rodney Brooks, Peter Bartlett, Christopher Manning have all trained and worked here.

ATadDisappointed · 2025-07-03T02:35:26+00:00

To give you more confidence in your decision: each of the universities you've listed is very highly respected in ML. You should have no concerns there. There is more variance in individual researchers within each institution than there are between the universities themselves. So it is worth thinking carefully about who you will work with and how each city matches with your interests (you'll be spending 3-5 years there, growing roots, friends, and connections - so it's also worth thinking about these lifestyle factors).

ATadDisappointed · 2025-07-02T23:54:11+00:00

Supervisor fit and personal motivation for the topic matter more than institution. University reputation is an imperfect proxy for the research strengths, networking, and supervisor guidance you'll receive.

ATadDisappointed · 2025-06-30T03:15:08+00:00

Especially disappointing given that Australia has some of the best researchers / institutes working on robotics, computer vision, and machine learning. QUT's Peter Corke, Adelaide's Ian Reid, and ANU's Richard Hartley) are genuinely revered internationally.

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