@JamesMaguire A9: There still seems to be a dearth in data science expertise. So we need more expertise. Lots of students getting trained up now and some autoML tooling will help to get to a reasonable model faster but still early days. #eweekchat https://t.co/1GRDwOXDBb
@JamesMaguire A8: It will certainly grow but it will need to adapt to be more explainable and proactively alert organizations for bias in their models/data. #eweekchat https://t.co/vsiM1DjzJa
@JamesMaguire A7: It's a large and diverse set of tech and depends on what you want to achieve. Some are good for out of the box regression models for getting started and other are better at more complex uses cases like finding similarities i... #eweekchat https://t.co/xVVnMjdf4g
@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. #eweekchat https://t.co/XzHdDQQ0Nn
@JamesMaguire 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. #eweekchat https://t.co/A3Nuk1Mt7c
@JamesMaguire 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. #eweekchat https://t.co/aKZAD1gANv
@JamesMaguire 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... #eweekchat https://t.co/PQaq0REiEB
@JamesMaguire I guess that's what I'm saying is that it needs to move towards being a glass box vs black box which is currently is now. #eweekchat https://t.co/NeHnBKZ213
@JamesMaguire A1: The number one thing for AI is the need for transparency. By turning the AI black box into a glass box, individuals can turn insights into actions and make better decisions. This empowers humans and business to be more effic... #eweekchat https://t.co/NEwWbtqzqd
A10: Maybe not related to the realm of computing but solid state batteries will drive up EV adoption with greater storage density and faster charging times! #eweekchat https://t.co/6b8EHG9Kxh
A9: And increases touchpoints between applications, compounding these problems and leaving behind those who are not automating. In 2022, it is no longer optional to invest in digital automation and transformation #eweekchat https://t.co/AmySiEOWe5
A9: Grow! In 2022, digital transformation continues across the enterprise. Lots of legacy applications still need to be transformed. #eweekchat https://t.co/4MZRabNesF
A6: No, not to it's deafening hype but AI will transcend marketing/cust svc and add value at the core of businesses. We’ll see greater movements toward explainable, transparent AI that mitigates bias and allows automation to support humans in... #eweekchat https://t.co/1ffYPs5TSm
A4: RPA will slowly cede popularity to data in motion boosted by easy-to-use and accessible APIs. It won’t happen right away, but once developers and data scientists solve how to get the needed data in tandem with digital transformation effor... #eweekchat https://t.co/rJQD6SEg4c
A3: Explainable AI eliminates the “black box” problem and offers greater auditability, transparency and confidence by expanding on the “why” behind automated decisions and predictions. #eweekchat https://t.co/ZawIHaUrfo
A3: Improving AI explainability - According to PwC, 50% of US business and tech execs say responsible AI that improves privacy, explainability, bias detection and governance is a top priority for adoption. So, I think Explainable AI will cont... #eweekchat https://t.co/Sa7PNWR7ON
A2: Data in motion will create greater innovation in automation and enable real-time #decisionmaking with data-backed insights #eweekchat#eweekchat https://t.co/Gz7nJ3YwXf