eWeekChat

A8- Skynet. That it will think like a human and hurt us. I don’t think so. Next would be “My business does not need an AI strategy”

@brianpodolak “My business does not need an AI strategy” -- I'm worried about the companies that say this.

@JamesMaguire It amazes me also....Or they think they have it already..

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The big myth is that AI is smart. It isn't smart, it is patient. It can get their arms around data and see patterns that humans can't.

A8. That it is in any way “intelligent”. These are functions that self-optimize to be good at a narrow task by looking at a lot of data and doing a bunch of math on it. It doesn’t know anything about how the world works, just what’s in the data.

@steveshah No doubt that AI is good at things that humans aren't -- and vice versa. It's a partnership.

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@brianpodolak Exactly. We are nowhere near sentient AI.

A 8. AI will take over jobs. Instead, AI will create new opportunities for employees and minimize mundane tasks, thus allocating human talent to higher-level tasks and roles. Companies can help by offering #reskilling programs to employees.

A8: Progress is task specific AI (eg. NLP, Vision) does not always correlate to progress Artificial General Intelligence (AGI) and despite common belief - AI will not take away jobs. In fact, it will create more job opportunities. - G.T.

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@steveshah I agree!!! Not even close...

For those interested, the "Man Machine Symbiosis" paper by J. R. Licklider is an interesting insight into human/machine partnership. (from a 1952 point of view)

Shout out to "The Dream Machine" by Mitchell Waldrop. Great read on how we got here. https://www.amazon.com/Dream-Machine-Licklider-Rev...

A8: That machine learning is AI. ML is more an automation tech now and a stop on the road to creating human-like intelligence — or AI.

A7 - I think its great. It only advances the possibilities faster. The democratization trend is mostly confined to enterprise environments but is likely to continue to spread, reaching a consumer base in time.

A 7. The introduction of #LCNC is changing software development into a simpler process with less turnaround time, revolutionizing the software industry by shortening the learning curve and making software development more quick, simple, accessible, and effortless.

Well, I live it. :-) Automation tools are key junctures for integration to workflow. They also remove much of the complexity in adoption. #RPA, #iPaaS empower business users to consume AI without API know-how.

A7. Putting no code modeling systems in the hands of business users is a powerful way to ensure models are aligned with business needs, if they're provided the guardrails to avoid data science mistakes they may not be aware of and good data to work with.

A7: This is far away. AI is an emerging technology. The democratization of AI can only come after companies can themselves understand and apply it.

A6 - Both. This is still a young science. For us its hard to get our heads around. Our grandkids will build them out of old computers and gas powered cars.

@brianpodolak I love that image!

A6. Most businesses will find their desire for efficient resource usage to be poorly aligned with cloud compute vendors, who make most of their money selling the compute time on which their AI platforms operate.

A 6. The cloud significantly supports the adoption of AI and API services, especially those available from the #hyperscalers, enabling both easier consumption and the capacity to scale AI workloads up and down at speed.

All of the above. No one company is going to be specialists are everything. There will always need to be niche players that specialize in one type of AI over another. For platform products like intelligent document processing, plugins will be key.

A6: Companies will source AI tools from market leaders. However, true end user-level AI innovation will need to be self-driven by corporations.

A5- Waiting. The advantage AI gives is incredible and at some point will become insurmountable by those who don’t deploy.

A5. Companies must avoid “AI for AI’s sake” and first establish the right use case application to guide their AI journey.

A5. Training a model is not the end of the ML lifecycle but the beginning. The world changes, sometimes quickly, and an ML model can go stale fast if left untended.

A5: AI with big data is a complex engineering and systems problem. Paying attention only to AI research, while ignoring the engineering, data and systems challenges will only lead to great POCs and not great products.- G.T.

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Bringing everyone along. the business win is always good, but at some point there will be a challenge. Feeling collective success tends to get over those humps with far more ease than going it alone.

@dgspjs +1 -- love this point. Models cannot / should never be considered static. If we're modeling human behavior, we have to remember that humans learn. So to must the model.

@sunil_senan - reminds me of "what is your linux/XML/etc. strategy?" No! no! no! These are tools, not solutions.

A5: Developing so-called AI tech without product development is an experimental exercise in data management and software development.

A4 - Start small. Then scale with familiarity and comfort....Sorry I am kind of repeating myself

A4P2 Also really needs to get that needs analysis solid before starting..

@brianpodolak Makes perfect sense. I think "start small" is industry accepted wisdom at this point.

@JamesMaguire You would be surprised!

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A4. Use model and prediction explanability to understand how your model works. Dramatically reduces the risk of model failures and can be a source of major insight into the business.

Program management 101... narrow focus, quick wins. Also look for use cases where there is a lot of data.

A 4. Creating an #AI road map to identify possible implications on the people, process, and technology – the challenges they will face, and possible resolutions they can turn to.

A4: Getting their Data Strategy, Data Quality, Data OPS right. AI is only as good as the underlying data it is built on. Investment in Data maximizes AI ROI. - G.T. #eWeekChat

(cont'd) e.g., applying AI to transactional data is usually interesting because every company usually has a lot of it

@steveshah Love this thought, and opens a huge can of worms...but very exciting for sure!

A4: Well-staffed and well-funded data science, data management, and software development are critical to developing true AI innovation.

A3 -Getting their head around it, first. Then when employees see it, it becomes a threat to their livelihood. A lot of "flying unicorn" expectations.

A 3. Scarcity of talent, risk-averse cultures, and an inability to imagine what the solution will look like on completion.

@sunil_senan Scarcity of talent is a big one. Price of talent is the problem.

A3. Getting the right data for the desired business case is always #1.

Transitioning models (and their data transformation pipelines) from training to production can be an Ops nightmare.

Diagnosing issues with black box models when something goes wrong. Kno

Even knowing that something in a production model has gone wrong.

Agree with Brian. Setting expectations is key. A lot of end consumers of the technology still have an impression of AI based on the movies. The other side is expectations - AI isn't magic, but it is patient.

A3P2 - I think you should also implement in stages..baby steps..don't try to replace a complete process...start small.

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@steveshah "AI based on the movies" is very real. It's magic! Though it's not....

@sunil_senan These are business drivers for sure. The desire for AI from leadership is key. Getting the broader organization on board has to happen too

Totally agree. I still run into a lot of "AI as magic wand."

:-) Per Short Circuit... "It doesn't get happy, it doesn't get sad... it just runs programs."

A3: They believe ML is true AI when it is not. They need to learn how to apply ML at scale organization-wide before materially tackling deep learning (DL) applications and human-like AI.

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Also nurturing that talent within business and IT is equally important. Digital transformation and talent transformation go hand in hand.

@steveshah Completely agree. This is key to drive broader alignment and culture across the enterprise, but it also helps to drive AI adoption and prioritization to solve meaningful problems.