GetReady4AI

Karl, we would love to host you at our HPC & AI Innovation Lab in Austin to see for yourself.

Karl, great question, we can offer our partners expertise

@Kevinjgray58 Karl, we would love to host you at our HPC & AI Innovation Lab in Austin to see for yourself

Sorry hit send too soon, we can work with partners on the use case, choosing the correct framework and libraries. Plus, we can help you understand how to get that AI workload into production at scale.

Karl - You are always welcome to visit our state of the art lab..:)

Karl, let me know if we can talk, partners a

Thanks, I would like to evangelize this offering to our partners, many of whom are keen to mine data, but don’t know how to get started. The idea of an AI bundled solution eliminates a major hurdle, however, a cost analysis of this versus AWS or Azure would help. Got one?

infrastructure cost and lack of trained staff serve as the big roadblocks right now

.@plburris The new innovation cocktail = Data + #AI + Cloud scale so constraints are primarily data quality & accessibility (i.e. no stovepipes), AI skills & resources (talent)...access to cloud scale is pretty much table stakes by now but the other two are significant barriers

AI is a new technology, and needs to be tested first in relatively bounded applications

@dfloyer agreed. Organizations are at various stages of the AI adoption journey, but almost all start with some sort of sandboxed environment at first.

@dfloyer Starting with a really well defined use case, usually a piece of software is so important.

It's actually a set of technologies with long vintage. What's new about AI is the extent to which it's been deployed into operational business applications and infrastructure, and the extent to which it's the core focus of developers for all types of disruptive apps

@dvellante Access to automation throughout the AI pipeline is as critical as cloud-scale. It's all about speeding up the DevOps cycle for these AI-driven apps, bringing greater consistency, industrializing the process 24x7 in enterprise development/IT shops.

Government / aerospace / defense use cases

Automating data workflows

Healthcare is ripe for disruption - but really hasn't been disrupted yet - really interested to see how machine intelligence applies in the near-to-mid term there #AI

we see a lot of interest in applications that can use image or video processing to improve business outcomes. For e.g. using pictures of components as they come into a manufacturing plant to scan for defects before they are used

Also interested in: 1/ Will machines make better diagnoses than Docs? 2/ Will driving your own car become the exception? 3/ Will large retail stores mostly disappear? 4/ What's the future of warfare and what role will #AI play?

improving operational efficiency, reducing risk, and improving security have been leading the way

@MikeLeone_IT +1 on the operational efficiency. Freeing up resources to do more creative and higher value activities.

Personal favorite AI use cases are in healthcare. Already useful in object classification (e.g. tumor detection), coming soon for NLP transcription of EHRs, and eventually full NLP on patient oral responses, prediction of illness onset, better treatment plan suggestions, and more

.@JayBoisseau One of the most incredible uses in healthcare I’ve seen @TGen is delivering precision medicine based on the Human Genome just inspiring. #AI will further transform this field. #IWork4Dell
https://www.youtube....

@NickBrackney Love the @TGen folks, know them well.. and talking to them about next steps in using #AI :-)

Thanks for the great partnership

@jamonascone Machine Learning Knowledge Center featuring the latest developments in artificial intelligence, machine learning and deep learning

Thanks for the partnership

This key point seems to get overlooked WAY too many times! It was a point that we tried to stress on my last few international consulting assignments. Especially with the GDPR rules in place, the data has to be accurate.

Yes Keith... #AI doesn't solve GIGO problem #DataQualityMatters

Yes. here's my discussion of the quality conundrum surrounding this data in an AI/ML development context, from a few years ago in another life. Data quality for social media analytics: sentiment or sediment? http://www.ibmbigdat...

Data Preparation can be 2/3rds or more of the total effort/time in an AI/Deep Learning project! Need to account for that!