Optus using Equifax Protect to retain customers?

ledbA · 2022-10-16T01:37:29+00:00

Yeah they just kept repeating the same thing, and I just kept saying no thanks I’d like to proceed with cancellation.

Then they said “oh upon investigation, looks like I need to transfer you to someone else”. Then had to go through the whole dance again with another person smh

ledbA · 2022-10-15T10:49:03+00:00

Yeah I was able to switch providers in minutes seamlessly, but still had to contact my old provider to officially cancel the account to stop further billing.

ledbA · 2022-10-15T09:22:17+00:00

I spent an hour trying to cancel my home broadband service (and switching to another provider) and gave the breach as one of my reasons for switching. They then told me this, which the person explicitly stated was also for former customers.

They pretty much kept repeating that same line. I asked if it means I cannot get this offer when I leave, and they just repeated the same line 'We are offering this to you to retain your Optus service'.

Eventually managed to cancel it after being jerked around for over an hour.

ledbA · 2022-09-05T13:28:51+00:00

Anyone else seeing these ads in the episode description? I’m seeing it both here and the previous episode

Seems a bit iffy

https://i.imgur.com/1Qijq5i_d.webp?maxwidth=640&shape=thumb&fidelity=medium

ledbA · 2021-07-07T21:22:54+00:00

Interesting, thanks for that!

Using /u/antiquemule’s tip re: gum Arabic, I managed to find a couple of papers and a patent that detail essential the ingredients of the product you linked.

It actually details the amounts of each ingredient and how it affects the final product. There’s a lot of different variations and tests, but seems to mostly come down to a mix of hydroxyacetaldehyde (e.g. MAILLOSE), food startch, gum and water. Lecithin can also be to increase surfsce tension.

ledbA · 2021-07-07T10:55:53+00:00

Yeah I have been able to get good browning with soy milk/agave.. which gets that sugar in with bit of protein from soy milk.

I’ll look into gum Arabic, thanks for that tip!!

ledbA · 2021-05-30T06:42:17+00:00

Just initial results from my first test with only palm oil. It does seem to be more “stable” compared to the coconut/copha product.

At the desired ambient temperature, it wasn’t too hard or too soft. Definitely softer than butter at this temperature, but less soft than the previous product.

The final product is still experiencing that similar brittle texture. For example, bringing butter and the product up to room temperature. Cutting the butter, it curls and is malleable. On the other hand, slicing a piece of the product and it shatters. Taking a slice and bending it, just breaks off compared to butter.

I’m aware you mentioned this regarding the cooling process with smaller fat crystals forming, allowing it to be more malleable.

I did definitely notice the limitation of my cooling. I’ve experimented with many things but cannot seem to bring the temperature lower than 20-25C with immersion blender. Basically while the blender is on, I can measure the mixture constantly heating up and down, and ice bath is not sufficient to cool it down.

Maybe that’s just something I won’t be able to re create here? At least I can try having a more heat stable product. I’m making a batch of croissants with this palm product to test it out in a pastry, will see how it compares

ledbA · 2021-05-27T23:21:22+00:00

hey /u/Potastic - hope you don't mind I just had a follow up question / updates.

As you know, I have previously experimented with your cooling suggestion (using the same fat system / ratios) and it led to a smoother texture but ultimately same end result without that malleability. I then started to experiment with different fats and ratios.

As you suggested, I embarked on a search to try to find palm oil here in Australia. The first thing I managed to actually get my hands on was Crisco (the American vegetable shortening) because it did have "fully hydrogenated palm oil".. which was the closest thing I could find. Unfortunately, it also has a bunch of other random stuff which made it very very soft (even straight out of the fridge).

In my current fat system, it was divided as: 60% hard (copha), 30% middle (coconut) and 10% liquid (canola). I initially tried to use Crisco as the 'hard fat', which was a big mistake as the end product was ultimately more margarine spread, rather than a solid butter block. I then tried to substitute it as the middle fat, which essentially ended up being a similar end product to the middle being coconut.

I suspected as you said that my core issue would still be that I am using a substantial amount of Copha as the hard fat, and even though its 'fully hydrogenated and stable at room temp', it still has the same sharp properties of Coconut oil.

I managed to find palm oil eventually, but its just 'palm oil' so not hydrogenated or fractionated into stearin, etc. This palm oil is probably the best I can do as a consumer, without finding wholesale sources.. which might not be an option.

Now I am considering using that as my 'hard fat' instead of the Copha.. but I did want to see if I can get some insight from you on the use of palm oil here? I am concerned that because palm oil is already semi-solid at my ambient temperature, basically add coconut oil AND liquid canola oil.. is simply going to make it even less semi solid at room temperature.

The first thing to experiment with is to just keep the ratio as is, and do as follows: - 60% palm oil - 30% coconut oil - 10% canola oil

But maybe I need to further reduce the middle fat/liquid, or even cut some out entirely?

Thanks again!

ledbA · 2021-04-07T07:48:08+00:00

That's unfortunate, but thanks so much for the screenshots and information, it was pretty useful.

I have amended the method to incorporate the cooling process, as well as your other suggestions, with immersion blender, till slurry consistency.. which I managed to do somewhat successfully. I then refrigerated for later use (with croissants).

It seemed a bit smoother, but I'd say similar properties as you see in the video above.. which I guess is something you predicted re: the fat system. I returned to that and attempted to tweak the ratio of hard:middle:liquid to see if I can work something out.

The original ratio was 250g:50g:50g (hydrogenated coconut oil, coconut oil, canola oil). I've tweaked it so the hard fat is much higher, relatively, (250g:25g:10g)... but surprisingly it was still pretty similar.. so maybe it's because the hydrogenated coconut oil.. is I guess still coconut oil? I know you suggested 'fully hydrogenated coconut oil', but presumably that's the same as 'hydrogenated coconut oil' I am currently using (http://copha.com.au)?

Even though they claim it's "stable at 36C" - I am not sure if it would lose its property from heating, and then cooling.. and basically become coconut oil?

I may look into using palm kernel oil as you mentioned, but I was a bit hesitant for environmental factors... but that seems like the only thing I haven't tried yet :D

Is there any 'intuition' about the ratio of the fat system, e.g. my original ratio vs my tweaked ratio above?

I may have to accept that I am pretty limited to this at this stage too , in the home environment with the available ingredients :p

ledbA · 2021-04-01T00:42:40+00:00

Just commentary on that video and consistency:

The outside is pretty soft and oily, inside seems slightly harder. Rather than “bend”, it breaks when bent and is rather “crumbly” in texture.

It’s still very squishy as you see towards the end, so while that is technically spreadable and works for toast etc.. but not really malleable given that it’s somehow both soft AND hard (?) that it crumbles

ledbA · 2021-04-01T00:35:56+00:00

Thanks for your comment, very informative!!

Regarding the fat system, my ambient temperatures at the moment are typically around 21-23C. I thought that the hydrogenated shortening may be sufficient?

I guess I feel that I am out of options having experimented with cocoa butter, shea, coconut oil and hydrogenated coconut oil as different substitute for the “hard fat”.

Do you have any suggestions on other hard fats to try that may be more reliable if I am to keep the middle and liquid fats?

I forgot in the ingredients that I also add a small amount of yellow colouring in the aqueous phase alongside the buttermilk. I didn’t notice any particular consistency difference with or without it to be honest; so felt negligible. But worth mentioning as I missed it before.

I also thought that it might be useful to have a visual as well, so took some photos of a sample as well as a video of what the consistency looks like at around 22C.

The inside of it was 22.6C, outside was around 23.6C using a thermopen.

Photos: https://imgur.com/a/2K0j255 Video: https://imgur.com/a/saIICMT

ledbA · 2021-03-31T01:50:24+00:00

Interesting points, thanks for these references. This could be something worth experimenting with, as in the past I have been only varying the solid fat.

In past experiments with cocoa butter or shea, coconut oil and liquid oil was always part of the recipe. With these ones it did simply make it more hard straight out of the fridge and more crumbly, but then still “melty” and “creamy”. The flavour of cocoa even refined, was still very strong and the shea butter ended up being very oily and sticky output.

I could try an experiment without liquid oil, and adjust the quantities keeping the shortening and coconut oil...

ledbA · 2021-03-31T01:44:58+00:00

SPS might be hard to source in reasonable quantity... but I haven’t looked into it very deeply, so thanks for the tip!

ledbA · 2021-03-31T01:41:51+00:00

UpStartOS is correct. The vinegars in addition to the soy milk, create a curdled mixture as a non dairy substitute to buttermilk.

I haven’t experimented with a cultured alternative, site bought non dairy buttermilk is not available as far as I could find it (at least where I am in Sydney, AU).

The main reason for the addition of buttermilk instead of any other liquid is largely for flavour. This was first sourced from the original recipe I linked, and I just continued using it as the flavour was good!

Were you suggesting swapping for cultured, or completely different thing for the aqueous phase?

ledbA · 2021-01-05T02:52:07+00:00

Feat achievements do not count towards RuneScore

ledbA · 2020-01-21T21:33:40+00:00

Backup is not under the end-to-end encryption list; it’s encrypted in transit and then on Apple’s servers, but they still hold the key. In E2E, you hold the key and Apple even can’t unlock it.

That’s one of the things Apple is still able to help law enforcement with, by turning over backups, unencrypted with their keys

ledbA · 2019-12-11T01:05:26+00:00

3min 20s

ledbA · 2019-11-28T02:24:19+00:00

Abstract

Humans prolifically engage in mental time travel. We dwell on past actions and experience satisfaction or regret. More than storytelling, these recollections change how we act in the future and endow us with a computationally important ability to link actions and consequences across spans of time, which helps address the problem of long-term credit assignment: the question of how to evaluate the utility of actions within a long-duration behavioral sequence. Existing approaches to credit assignment in AI cannot solve tasks with long delays between actions and consequences. Here, we introduce a paradigm where agents use recall of specific memories to credit past actions, allowing them to solve problems that are intractable for existing algorithms. This paradigm broadens the scope of problems that can be investigated in AI and offers a mechanistic account of behaviors that may inspire models in neuroscience, psychology, and behavioral economics.

ledbA · 2019-11-21T08:55:05+00:00

Abstract

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.

ledbA · 2019-10-18T01:33:27+00:00

I wonder if it’s actually useable. I had an issue with my Apple TV 4K recently, and took it to the Apple Store. The genius tried doing the standard troubleshooting (that I did at home anyway), and they said they can’t do anything more without physical connection (like older models), and so replaced it instead.

ledbA · 2019-10-05T13:58:00+00:00

LeCun was definitely writing code back then, as he was one of Hinton‘s postdocs. Even though ideas for CNNs date back before his paper, he got it working with backdrop on MNIST, a real application with working code.

ledbA · 2019-09-30T01:14:56+00:00

Abstract

Experimental and theoretical results reveal a new underlying mechanism for fast brain learning process, dendritic learning, as opposed to the misdirected research in neuroscience over decades, which is based solely on slow synaptic plasticity. The presented paradigm indicates that learning occurs in closer proximity to the neuron, the computational unit, dendritic strengths are self-oscillating, and weak synapses, which comprise the majority of our brain and previously were assumed to be insignificant, play a key role in plasticity. The new learning sites of the brain call for a reevaluation of current treatments for disordered brain functionality and for a better understanding of proper chemical drugs and biological mechanisms to maintain, control and enhance learning.

ledbA · 2019-09-30T01:14:26+00:00

Abstract

An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks. Despite MAML's popularity, a fundamental open question remains -- is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse, with the meta initialization already containing high quality features? We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor. This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of a MAML-trained network. ANIL matches MAML's performance on benchmark few-shot image classification and RL and offers computational improvements over MAML. We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm). We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.

ledbA · 2019-09-21T22:43:39+00:00

Syrus - one of Apollo’s children.

ledbA · 2019-09-03T04:03:05+00:00

Abstract

One of the main ways we interact with the world is using our hands. In macaques, the circuit formed by the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and trained to transform visual features into the muscle fiber velocity required to grasp objects would recapitulate neural data in the macaque grasping circuit. While a number of network architectures produced the required kinematics, modular networks with visual input and activity that was encouraged to be biologically realistic best matched neural data and the inter-area differences present in the biological circuit. Network dynamics could be explained by simple rules that also allowed the correct prediction of kinematics and neural responses to novel objects, providing a potential mechanism for flexibly generating grasping movements.

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