eWeekChat

EASY ANSWER! - the AI will replace us human workers!

A5: AI is coming for human jobs. It’s NOT. AI/ML works best when humans are in the loop. Leveraging human accountability within AI/ML leads to better governance, predictability of customer needs and more personalized solutions.

@RyRaiker Well, it will replace "some" of us.... won't it?

That there's no room for innovation outside the big tech firms.

Assisting, Not Overtaking
We shouldn’t fear automation, machine learning, and artificial intelligence in the workplace. These tools are going to reshape the way we live and work in a meaningful way, but this is very likely to be a positive impact. #ai

A5: The biggest myth is that data scientists strongly believe their models are so unique that no commercial software can handle their unique properties.

This is not the case, and it stems from a knowledge gap that exists between teams.

If it does replace some of us, there will always be other work to do.

A5: Myth: you can only solve #AI challenges with data technology & tools. Facts: #DevOps is critical when dealing with the demand, integration & maintenance issues while moving AI & #ML artifacts from deployment to production. Hence the need for #DataOps, #MLOps & #AIOPs

Hmmm. Yes and no. Most of the projects we were work on with our customers result in AI solutions that augment people's jobs

@RikChomko I want to believe that. But what about the untrained workers it replaces?

@RikChomko When you think about the building trades, do you think that the drill replaced the job of the human carpenter? I imagine the answer is: Absolutely not! I think everyone would agree that the drill assisted the carpenter and made him much more productive in his trade

A5: That the marketplace and society are ready for true AI and its ripple effects.

@Chris_Ehrlich Chris, so true. We aren't ready.

Re AI replacing humans. I think, yes and no. Most of the projects we were work on with our customers result in AI solutions that augment people's jobs

@Adewunmi Agree. We see #AI-based "co-bots" much more prevalent than robots across all settings.

@Chris_Ehrlich This is such a good point. It's why I am really excited by the work that civil society is doing in this space. For example, algorithmic Justice League's work on Algorithmic Bias Bounties.

@Ryraiker That is the way I think about it. Yes, there are some skills required and yes there will be some unskilled workers that are replaced. Other jobs should arise for them and opportunities for retraining.

@BCorrIoT YEah. I also think growing agitation for regulation as the public gets savvier about uses of AI and how it affects them will also have an effect on ideas about full automation and replacing people.

A4: An audit can help pre-determine what a successful #AIOps implementation looks like for an organization. This includes aligning your AIOps initiative to address current needs such as more effective event noise reduction & faster probable cause analysis.

A4: Start with the fundamental. What business problem are you trying to solve and why AI. Some business challenges may not require sophisticated AI models.

A4: This is a little counterintuitive but I would say don’t focus on just AI deployment. Take a holistic approach and assess your entire machine learning lifecycle. Think MLOps alongside questions of how can I deploy more quickly and effectively.

@victorthu I agree. Just because you have a AI hammer, doesn't mean every problem is an AI nail.

A4: Data drives change! Organizations need to look at how processes are actually occurring. This is done by looking at the timeline of events in a process. It can also find blind spots and common bottlenecks prime for AI fixes

A4: Invest in DL and true AI now as a differentiator, as ML know-how and execution will become more standard and buyable.

(edited)

@Adewunmi It's true that AI strategy has to consider the whole. It's not a stand alone project.

@BCorrIoT I always love this saying. People think they solved it all with one tool - they say it takes a village and it does, it truly does

@BCorrIoT Hard agree! And I think an excessive focus on PoCs can sometimes make it easier to miss non-viable ML projects/ use cases

@RyRaiker " Data drives change!" Basically the motto of our era.

And the other thing is, don't fall in love with super high accuracy with your models. Sometimes the actual business results between 80% accuracy and 90% accuracy is not material. It's better to just deploy them!

beyond all that .. maybe “keep it simple” - You know … “hey team let us not try to boil the ocean” even small AI wins can make a major impact on the enterprise

Yes, and that means data ingestion, it means recognising and planning for differences between the conditions in your development environment versus production. It also means taking a more cross-functional team approach to developing AI solutions

Exactly @RyRaiker!! You can definitely get AI wins starting with taking smaller bites.

A3: Getting overrun by data and not first solving enterprise-level data management. Assembling strategic talent in data science and analytics.

A3: 1) The over-romanticization of free open-source tools to deploy AI/ML. It’s hampering enterprises from getting any real ROI.
2) The scarcity of talents and the difficulties of hiring the right talents.

@victorthu Talent scarcity is HUGE in AI. Expensive talent.

A3: #AI pain points range from data reliability, AI sprawl, auditability & ethical challenges around privacy & potential algorithm biases. The first step is to ensure you have reliable, unbiased data while adopting proper #DataOps & #AIOps best practices.

A3: I think 2 big pain points here are (1) not having standardised practice for MLOps (2) not being able to take PoCs into production

Exactly @JamesMaguire !! And when you couple of that with people who want to build their own MLOps platform with the scare talents. It's a recipe for failure.

A3: Complexity is slowing progress along with wrapping your arms around all the data. The projects expand scope and encounter model drift and to some degree trying to get to a perfect model.

A3: I think another emerging pain point (which in some ways is an expansion of bullet 1) is not have clear frameworks for deciding when to build or buy off-the-shelf. Is your use case generalizable enough to make off-the-shelf viable? If not, can you make it so?

I agree, scarcity of talent all realms of data engineering and science is a major problem.

@BCorrIoT Totally agree on algorithm bias. It's a real challenge.

@Adewunmi And deciding to build is fraught with hazard. Big expense.

A3: I agree with issues others' have raised, I think in the past #DataOps has been underestimated. I think that's changing now. Or at least orgs are waking up to its importance.

Yes, I agree but understanding and clear articulation of business value is the starting point for deciding whether it's worth it. In our consulting practice, this is one of the questions we make sure to ask customers or help them work out.

A2: Companies’ comfort level with AI is still very early. I was recently talking to an enterprise that is known to have a massive AI team and have done a lot to incorporate AI in their business. Only to find out most of the AI models are still in the lab.

A2: #AI was once considered a back-office fundamental. But with the accelerated pace of digital transformation, #IT service & operations teams are using #AIOps to become more agile & proactive, to better anticipate challenges.

@victorthu Not surprising at all! But perhaps a waste of money??

A2. Companies are starting to get more comfortable with AI but it still has a ways to go. A recent Forrester study revealed that in 2019 only 54% of companies were using some form of AI, in 2020 that number grew to 69%. So again movement in a positive direction.

I think there's a spectrum here. I thinks orgs are definitely comfortable with the *idea* of AI. So there's maturity in that sense. I don't think we have hit maturity when it comes to identifying all the possible ML use cases within their operations.

A2: The #AI market is still very nascent, especially when operationalizing #IT.

@BCorrIoT #AIOps definitely on the upswing.

A2: Having said that, conversations about AI are getting easier, more realistic and more holistic as well

Yes, @JamesMaguire, there's definitely a sense of concern especially the lack of ROI with such a huge investment. So if a large company is struggling, the smaller ones will feel the pain even more acutely.

A2: They’re getting practiced in ML mostly. DL and true AI are frontier tech far from commercialization across the market.

@victorthu AI still feels like a tool for larger enterprise. But the SMBs are eager to catch up.

A2: By holistic, I mean customers are more open to thinking about AI as much more than primarily model development.

Yes @Adewunmi , very true. Compared to a few years ago where many AI vendors are selling vaporware vs. today more enterprises are better educated on the topic.

definitely. A recent study showed that 41% of enterprise IT use over 10 tools for performance management and monitoring. Hence the need to introduce #AIOps.

A2: Companies have become more comfortable using AI within the past 3-4 years, mainly attributed to the popularity of RPA. But, by itself RPA is not AI

definitely. A recent study showed that 41% of enterprise IT use over 10 tools for performance management and monitoring. Hence the need to introduce #AIOps.

@BCorrIoT That is a blizzard of tools. I think larger companies in particular want "best of breed" on everything. Which does drive AIOps.

@BCorrIoT Companies have also become comfortable using chatbots, but those who are digital natives spot them right away and the younger population gen-z and millennials tend to hate them! #ai #eweek

A2: maturity comes from collaboration and an ecosystem but not being locked to underperforming tools.

@RyRaiker The problem with chatbots is that they are artificial but not intelligent ;)

@RyRaiker not to mention increased collaboration between business and IT professionals in digital business initiatives demands new practices, policies and technologies.