David Walliker

Chief Digital & Information Officer @MFTnhs | Fellow @BCS | Honorary Chair Informatics, Imaging and Data Science @OfficialUoM | #datasaveslives |

[MFT NHS @MFTnhs]

Our Group Chief Executive, @MCubbonNHS recently spoke to @health1tech about the vital role digital has to play in our new five year strategy

He also spoke about how Hive, our EPR system, is delivering value for our patients

Read more: https://t.co/R9YBA2bJKT

[David Walliker @InsideHealthCIO]

@rs_fullerton @RachelBurnham The world isn’t ready for that level of bin bants.

[ARCHIVED - NASA's Perseverance Mars Rover @NASAPersevere]

What a week! Anyone else possibly discover signs of microbial life from billions of years ago on a planet 140 million miles from Earth?

No? Just me?

[David Walliker @InsideHealthCIO]

@RachelBurnham @MFTnhs Welcome to the team Rachel!

[NCSC UK @NCSC]

In response to today’s IT outage which has caused significant global disruption, we have published this statement, including advice on how to defend against phishing attempts 👇
https://t.co/k29nKVF7dB

[Imperial NHS 💙 @ImperialNHS]

A study with @OUHospitals has found that an automated AI voice system enhances patient care after cataract surgery ➡️ https://t.co/hdqUcNXM41

The AI-powered system calls patients to ask questions, understands their answers and identifies patients who may need review by doctors.

[David Walliker @InsideHealthCIO]

@rs_fullerton @Sonos @AppleMusic @oliviarodrigo @CrowdedHouseHQ @foofighters @taylorswift13 See - i will nod approval. I was going to Wembley to watch Taylor Swift but couldn’t only get 2 tickets and didn’t fancy being killed by one of the other 2 girls. Will approve 100% of foo’s. Crowded House would be in my top Antipodean bands, and Vampire is on the play list!

[David Walliker @InsideHealthCIO]

@rs_fullerton @Sonos @AppleMusic I don’t have one. But I have a top one of the ‘phonics. I mean to be fair, my top 50 wouldn’t have Coldplay in there. That’s the thing with music, it’s subjective. Love what you love.

But you’re wrong with CP 😉

[David Walliker @InsideHealthCIO]

@rs_fullerton @Sonos @AppleMusic It’s pushing Coldplay down so you can listen to much better music. Call it a public service.

[Manchester Royal Infirmary @MFT_MRI]

MyMFT is our patient healthcare app. 📱 💙💻

It connects you to your medical information at Manchester University NHS Foundation Trust hospitals.

Learn more, and sign up today, at: https://t.co/O6LFZdnCSV

#HealthcareinYourHands

[David Walliker @InsideHealthCIO]

@GaryMcAllister Build our own? Goodness no.

[David Walliker @InsideHealthCIO]

@digitalhealth2 Edit: SIX YEARS ago.

[David Walliker @InsideHealthCIO]

@digitalhealth2 https://t.co/hajeSuLmBv FIVE YEARS ago. So if we still need to explore this, something’s wrong with the navigation.

[David Walliker @InsideHealthCIO]

@GaryMcAllister Yes.

[David Walliker @InsideHealthCIO]

@PaulJBtW @FD_DSM1 Is this like flu jabs, if it wasn’t on social media it didn’t happen….

[Kimberley S-Jamieson @Kimberley_S_J]

⁦@InsideHealthCIO⁩ ⁦@MFT_MRI⁩ ⁦@MRI_ED⁩ #ExcellentVisitProjectRed #ExcitingForPatients&Staff

[Arvind Narayanan @random_walker]

New AI Snake Oil essay: AI scaling myths
Scaling will run out. The question is when.

Full essay: https://t.co/uaaAaHrhHr

Summary:
Scaling laws only quantify improvements in next-word prediction, not emergent abilities. What matters is emergence, and it is not governed by any law-like behavior.

There's a reasonable view that LLMs can't extrapolate too far beyond their training data. If so, at some point, having more data no longer helps because all the tasks that are ever going to be represented in it are already represented.

Besides, there are limits to how much data companies can get. It's not that they'll "run out" of training data — there’s always more data, but it will cost more and more.

Seemingly exponential tech trends have a tendency to suddenly flatline. Scaling is ultimately a business decision and fundamentally hard to predict in advance.

Synthetic data is not magic. It has many great uses but increasing the volume of training data is not one of them.

Self-play has been spectacularly successful in self-contained environments like Go but won't work everywhere.

Based on current market trends, building bigger models is hard to justify because the barrier to adoption of current models isn't capability, it's cost and other factors.

OpenAI, Anthropic, and Google have all made their frontier models much smaller recently, if we use API pricing as a rough proxy for size.

Unlike dataset size and model size, training compute continues to scale (smaller models require more training to reach the same level of performance). The earlier crop of models were under-trained and the current generation is being trained for dramatically longer, which is better when accounting for inference cost.

In the AGI chapter of the AI Snake Oil book (https://t.co/foQpEhRfhs) we conceptualize the history of AI as a punctuated equilibrium, which we call the ladder of generality. Instruction-tuned LLMs are the latest step; an unknown number of steps lie ahead.

Historically, standing on each step of the ladder, AI researchers been terrible at predicting how far you can go with the current paradigm, what the next one will be, when it will arrive, what new applications it will enable, and what the implications for safety are. That is a trend we think will continue.

With @sayashk.

[David Walliker @InsideHealthCIO]

Finally admitted that age has defeated me and purchased varifocals. I do worry that I will be like the ED-209 from Robocop the first time I attempt stairs though.

[David Walliker @InsideHealthCIO]

@LawrenceDunhill he’s brilliant, but I probably wouldn’t have him in goal…..