#think2019 - CrowdChat

Learn from Cloud & AI Experts

IBM experts discuss a faster, more secure journey to cloud, how to accelerate your path to AI, and what you can learn at the upcoming Think 2019 event.

#think2019 @ericminick @ralphfinos @franklybriana @plburris @rreinitz @lawtonbill @jim_farrow @ramvennam @lori_pb @jeff_brent @bobspory @ankita03pandey @pwmccaffrey @wikibon @nottycode

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Establishing a secure connection

How is your enterprise modernizing your data and integration architecture to accommodate a growing mix of clouds, SaaS, and traditional data sources on and off premises? https://www.crowdchat.net/s/45sdy

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Peter McCaffrey

by evolving to an "Agile Integration Architecture" that rethinks people, process, and technology. Learn more : https://www.ibm.com/cloud/integration

IBM Cloud Integration
Learn how IBM Cloud Integration — including cloud integration services, hybrid cloud integration and cloud data integration — help you access and use critical data with API, application, message and data integration.

The key is implementing an IA architecture that enables moving code to distributed data.

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guidance available modernizing data & data as a differentiator https://www.ibm.com/cloud/garage/architectures/dat...

https://www.ibm.com/cloud/garage/architectures/dataAnalyticsArchitecture

I'm seeing many companies moving their ECM content into the public cloud as part of their modernization strategy. Check out the Business Automation Content Services on Cloud details in the Digital Business Automation sessions at Think.

There's another factor to consider here, the scores of real-time transactional government tax mandates that require the digitaltransformation of core financial solutions to keep up with the digitization of tax. Hence the first MarketScape on the topic released this week

...MarketScape on the topic this week.

Anantha Narasimhan2

Thanks for your time. We look forward to seeing you at THINK. Please feel free to reach out, in case you need any assistance

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see you there!

Not organizaition, but scientists should be responsible for minimizing model biases, because their PhD training give them enough knowledge to minimize model bias.

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Jason Tavoularis2

Come join my session at #Think2019 - AI in BI: Augmented Intelligence in IBM Cognos Analytics

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will do!

What is your organization doing to manage and mitigate bias in your models? https://www.crowdchat.net/s/05rjc

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Debiasing models starts with debiasing data. Here's a piece I published on the emerging best practices in this. From last year: https://www.informationweek.com/big-data/ai-machin...

This is an important trust issue! If a company is shown not be have addressed this issue, there are severe risk of brand damage. E.g., a store with cameras with AI to help employees meet, greet or challenge customers entering the store should be especially careful!

Bias in AI a very hot topic and critical. There are great examples, i will share later on how many societal biases are in our datasets. So we really need tools and technology to help on data traceability, explainability

always be training your models! Context injection mechanisms are poor with current toolings but we are aware of this problem, that means, we are on our way to solve it!

The best way to manage and mitigate bias in your model is to understand #statistics, #mathematics, #data, #science, #engineering and people

you have to remove bias from data input! Algos aren’t bias, data is! Always keep that in mind!

Carlo Appugliese

What I've seen is that companies are doing this manually after the fact and they are really falling short.. A bias model can occur without you knowing because of natural bias in your data.. This is a topic that needs to be considered in the beginning of your AI project.

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All models will have bias because we live in a world without perfect information, which is why being able to communicate the extent that the bias poses a risk is so essential in #AI

Most people think algorithms are objective but in large part they’re opinions embedded in code.
• AI systems are black boxes; the data goes in and the answer comes out without an explanation for the decision.
• Algorithms that learn are supposed to become m

Algorithms that learn are supposed to become more accurate unless bias intrudes and amplifies stereotypes.

Current ML models understand what's explicitly stated, but less good at anticipating what's not said or implied.

@sarbjeetjohal algorithms aren't in and of themselves bias, but it can increase the bias depending on how it is designed

Must develop effective mechanisms in algorithms to filter out biases and build ethics into AI with ability to read between the lines or what requires common sense.

@DameWendyDBE University of Southampton, Growing role of #AI in our lives is ‘too important to leave to men’ https://tnw.to/2whJyRx

https://tnw.to/2whJyRx

Developing appropriate reporting and monitoring for models and algorithms implemented in productions is essential for limiting bias

@jennjshin you mean under tooling! That’s true but bias is injected from data (once right algos are picked)!

Here's a session happening at Think 2019 that will dive into Detecting and Mitigating Bias in AI: https://myibm.ibm.com/events/think/all-sessions/se...

@sarbjeetjohal Good point - that could also be the case (although not always the case). Technically, there is more than one type of bias so the source of the bias depends on the cause of it (i.e. data vs algorithm). Happy to provide an example if you're interested :)

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That one looks like an interesting session to attend at #IBMrethink on removing bias from #ML and #AI, Jennifer!

@jennjshin I have said this few times in recent past. As of now, #AI #ML use has to be surgical (in narrow domains) you widen the domain, you widen the risk of unpredictable outcomes and greater bias (as context grows exponentially). #rethink

Are you analyzing your data to gain insights into what’s happening in your business?

Are you analyzing your data to gain insights into what’s happening in your business?

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Sarbjeet Johal1

What are the best practices for executing a successful Data Governance Program? Please share recommendations/resources from IBM in this regard!

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Come join us at THINK for the following sessions https://myibm.ibm.com/events/think/all-sessions/se...
Data Governance & Data Lineage at Hartford: https://myibm.ibm.com/events/think/all-sessions/se...

Anantha Narasimhan

One more - Cognitive Data Governance: https://myibm.ibm.com/events/think/all-sessions/se...

Anantha Narasimhan

Here's a blog you might find useful: https://www.ibmbigdatahub.com/blog/5-data-governan... . Please let us know if we can help in any way

5 data governance lessons from gardening
If you don’t know what data you have, how can you manage it effectively and generate value from it?

How are your analytics users using data visualization and low-code development tooling? https://www.crowdchat.net/s/55rj2

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Anantha Narasimhan

based on prior experience, when we want to accelerate self-service analytics, low code/no code become important

Increasingly, analytics developers are using declarative, visual, low-code tooling to program AI/ML, with the tooling leveraging auto-ML to compile models for optimized execution on target platforms.

I see two categories of analytics users: Data Scientists using dev tooling like jupyter notebooks and OSS visualization libraries, vs LoB users using canned reports and dashboards.

Analytics business users are also using self-service, visual tooling to build predictive and other advanced analytics for decision support--eg Cognos.

I tried using Tableaux, but gave up after a few days. Nothing beats Cognos especially after the latest improvements in 11.1

Anantha Narasimhan

with Cognos Analytics 11.1, Business Users can use natural language queries to get insights into data.. and stunning visualization to clearly state trends or issues (sorry - shameless plug in) :)

ML-driven augmented programming, leveraging low-code visual front-ends, is a huge research focus here at Wikibon. See my report from a year ago: https://wikibon.com/augmented-programming-brings-m...

I find more teams are using #datavisualization across an organization ranging from creating a realtime dashboard for the c suite to using it as a a tracking tool for day to dat operations.

Carlo Appugliese

One my favorite visuals is chord plots... :)

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Here's a great session that will showcase the new capabilities in IBM Cognos Analytics 11.1 and how it uses AI to provides smarter self-service analytics: https://myibm.ibm.com/events/think/all-sessions/se...

https://myibm.ibm.com/events/think/all-sessions/session/3651A

What companies in your opinion are great @ monetizing their data ( e.g. Uber, Facebook ) .. As economist rightly pointed out, Data is the new oil https://www.crowdchat.net/s/75riu

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I believe it’s Google and Amazon! Facebook blew it! Facebook is an example of what not to do. When you democratize data through API you want proper mechanisms to control the misuse.

@sarbjeetjohal I agree on FB .. they got greedy & blew it big time

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Anantha Narasimhan0

Good blog by @AliyeErgulen on how 3 organizations are using Business-ready data to drive transformation: https://www.ibmbigdatahub.com/blog/3-business-expe...

3 business experts, 3 use cases on business-ready data
Most businesses, independent of their business model, are concerned with compliance and profit. The business must comply with the law, regulations and conduct guidelines, and to be sustainable, the...

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Carlo Appugliese0

https://myibm.ibm.com/events/think/all-sessions/se...
Here is sessions at #IBMThink where Banco Macro will go into detail about their success leveraging AI to anticipate their customer’s needs.

https://www.ibm.com/blogs/business-analytics/rock-star-ibm-data-science-elite-team/

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