We’ve covered episodes about AI in logistics in the past. Let’s now focus our attention to manufacturing. Some AI applications in this sector include predictive maintenance, quality assurance using computer vision, anomaly detection, and digital twins. 

Building these solutions takes time and requires accuracy. If developed and operated manually 100%, this approach risks making more errors in your ML models and datasets. Rather than relying on grit, we can automate the entire process and let the AI solution run like clockwork.

MLOps (machine learning operations) automates the ML process together with the help of a feedback loop system. It can be divided into layers, like a layered cake. Some of its layers include experiment tracking, dataset monitoring, qualitative tests and explainability layer. 

In this episode, we talk to Marek Tatara, Chief Scientific Officer of DAC.digital as he tells us more about how MLOps works, and their experience in building a customized MLOps for the semiconductor industry under a large cooperative EU-funded project. 

Listen to our episode if you want to make your ML project more effortless and more reliable at a larger scale. 

Who is Marek Tatara?

Marek Tatara, PhD - Chief Scientific Officer and Tech Lead of the AI team at DAC.digital, Assistant Professor at Gdańsk University of Technology, AI/ML Expert at M5 Technology, Member of the Polish Society For Measurement, Automatic Control And Robotics. At DAC.digital, he works on the research agenda of the company and on the implementation of both EU-funded and commercial R&D projects from the field of Computer Vision (especially multi-camera setup, 3D reconstruction, and object detection and tracking), Machine Learning (mainly for computer vision, e.g., object detection, DNN optimization, semantic segmentation), and Embedded Systems (wireless communication for IoT devices and medical devices implementation.

Where to find Marek:

• DAC.Digital website

• LinkedIn

Resources:

• Book recommendation: Modern Control Theory by William Brogan

• Aims50 - Artificial Intelligence in Manufacturing leading to Sustainability and Industry 5.0

Time Stamps

(00:00:00) Trailer

(00:01:08) Who is Marek Tatara?

(00:01:50) AI in Manufacturing: Applications

(00:04:18) Concepts behind MLOps

(00:08:18) Custom vs pre-made tools for MLOps

(00:10:41) Building an MLOps project is like building a layered cake

(00:17:08) Adopting MLOps among small and medium manufacturing companies

(00:19:26) Working in an EU funded ML project

(00:21:06) Advice on AI adoption and implementation for SMEs
(00:26:11) Final remarks and book recommendations

--- More on G.M.S.C. Consulting

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Does your business need an AI computer vision co-pilot?

You might need it, especially if you’re working on a high-stake AI project that requires precision or accuracy.

In this episode, Akridata’s AI engineer Alexander Berkovich tells us more about it as he covers the different use cases that have used Akridata’s computer vision co-pilot. To name a few are corrosion detection, autonomous vehicles, railroad inspection, and industrial maintenance.

You can learn more about good data management and deployment practices to ensure a less biased operating AI model for your computer vision project.

Who is Alexander Berkovich?

Alexander is a principal AI/ML engineer at Akridata, whose tools and services save time and lower costs developing vision-based applications and systems. Previous positions include an R&D manager, team lead, and algorithm developer in a variety of domains, ranging from smart cities, to medical quality inspections, manufacturing and more, all in the computer vision space. His aim is to automate decision making based on a combination of visual sensors, software, hardware and the maths behind it all, to improve the quality of services, products and daily life.

In addition to focusing on the technical aspects of development, Alex advocates for the importance of grasping the business case and employing high-quality data, especially in this AI driven era.

Where to find Alexander:

• Akridata.ai

• LinkedIn

Time Stamps

(00:00:00) Trailer

(00:01:42) About Alexander and Akridata

(00:03:42) About computer vision copilots

(00:07:15) Use cases

(00:21:50) Importance of data quality when training models

(00:16:01) Model training and deployment, accuracy, precision and recall

(00:20:58) Dealing with clients’ needs

(00:24:44) Addressing biases in AI computer vision models

(00:47:44) Closing remarks 

--- More on G.M.S.C. Consulting

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Do you want to build the ultimate search engine that works perfectly for your business?

A multimodal system processes all types of information like images, videos, audio, and text. It can end up either as a Frankenstein or a Swiss knife depending on how clunky or how smoothly these models communicate with one another.

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, we had a chat with Duarte Carmo about building a customized multimodal search engine. We learned about his experience on building a multimodal search engine for a startup, and things you need to consider when building an AI product that does not rely heavily on big tech’s costly services. 

If you like tinkering and using non-conventional methods to build personalized AI products like a multimodal search engine, this is the episode for you. 

Who is Duarte Carmo?

Duarte is a Portuguese technologist who is now based in Copenhagen. He loves finding ways on making technology improve people’s lives. He solves problems end-to-end by combining his interests and work experience in Machine Learning, Data, Software Engineering, and People. If he’s not working on a project, you’ll find him learning about new gadgets, writing code, taking photos, or running.

Check out our ⁠show notes⁠ for more info on Duarte Carmo. ---

Time Stamps

(00:00:00) Trailer

(00:01:07) Who is Duarte?

(00:02:57) Use case: building a multimodal search engine

(00:08:23) Challenges of developing and using a multimodal search engine

(00:12:23) Human + machine: precision, reasoning and language

(00:16:34) Challenges of communicating between humans and machines: freedom vs restriction, bias vs. variance

(00:19:13) Why is it important to ask your client what their problem is?

(00:22:58) Building an AI product: iterative approach vs. perfect launch

(00:26:49) Priorities in AI development

(00:31:29) Are customized AI solutions affordable or too expensive for small and medium businesses?

(00:36:42) Privacy as a concern for multimodal search engines; APIs and privacy

(00:38:56) Duarte’s achievement of building a customized multimodal search engine

(00:40:14) Final remarks and book recommendations

--- More on G.M.S.C. Consulting

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How many times did you lose customers because you address them with your marketing campaign?

You sent so many freebies and promotions that they actually got annoyed and leave.

And how many times did you actually waste money in freebies and promos on customers that would have bought from you anyway?

The answer usually is a lot, but there is an answer to this problem, and this answer is called uplift modeling,an innovative machine learning technique that relies heavily on causal learning and causal inference, an innovative AI concept that leverages causal relationships between causes and effects, and not just simple correlations like 99% of machine learning out there.

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, we are going to discuss the ins and out of this novel and powerful approach with Aleksander Molak, author of the book, Causal Inference and Discovery in Python.

Stay tuned, because if you are a marketer, this is a game changing technology.

Who is Aleksander Molak?

Aleksander Molak is a Machine Learning researcher, educator, consultant, and author who gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, designing and building large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, international speaker and the author of a best-selling book Causal Inference and Discovery in Python.

He’s a founder of Lesprie.io – a company that provides machine learning trainings for corporate teams, and the leader of CausalPython.io community.

Aleksander has provided workshops, talks, and trainings for companies across industries, including market leaders like Mercedes-Benz, innovative disruptors like TechHub, international consulting companies like Lingaro and more.

Check out our show notes to know more about Aleksander, his book, and his work.

Timestamp

(00:01:29) Who is Aleksander?

(00:05:06) Machine learning vs. causal learning

(00:12:27) Potential application of using causal learning - uplift modeling

(00:22:45) Executing a causal learning project requires meaningful data

(00:26:14) Causal learning as a low hanging fruit for businesses

(00:29:41) Evaluating causal models

(00:35:19) Balancing the cost of experimentation

(00:38:26) Other applications of uplift modeling

(00:41:13) Preparing for a causal learning project with Lespire Consulting

(00:44:03) Educating data science teams on causal learning

(00:46:53) When should someone buy uplift modeling?

(00:50:07) Concluding remarks and book recommendation

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How can you use ChatGPT in business without risking your clients’ privacy? 

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, we sit down with Frederico Comério, the CTO and Head of AI in Intelliway.

We are going to chat about the  potentials, and risks of generative AI, and how we can make it safe for businesses.

Keep listening, and learn how to build a private, powerful and secure version of ChatGPT for your own company.

Who is Frederico Coméiro?

Frederico Comério is the Chief Technology Officer (CTO) and Head of Artificial Intelligence at Intelliway. He has extensive experience in project management and technical leadership within various domains, including software development, infrastructure, information security, and artificial intelligence. Frederico holds certifications in PMP (Project Management Professional), Prince2, Professional Scrum Master, Cobit5, among others. He is a graduate in Computer Science, with an MBA in Information Security Management and a postgraduate degree in Data Science and AI.

Check out our show notes to learn more about Frederico Comério.

Time stamps:

(00:00:45) Who is Frederico Coméiro?

(00:04:45) Why do you need AI for business knowledge management?

(00:07:11) Generative AI vs NLP for customer assistance

(00:09:33)  Information retrieval system: IT infrastructure, generative AI and prompts

(00:14:16) How does ChatGPT work?

(00:18:08) How do you integrate old formatted data with the latest AI systems?

(00:21:45) AI and data science projects: Do I go for DIY or ask for help?

(00:25:51) Book recommendation and closing remarks 

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If you asked AI to solve a Rubik’s cube, wouldn’t it be nice to understand all the steps it took to achieve the goal?

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, you will meet the other half of  AI: symbolic AI. It gives a step-by-step procedure that leads to its final answer. As pointed out by Abzu’s founder and CEO, Casper Wilstrup, symbolic AI helps pharmaceutical, financial, manufacturing, and logistical companies solve hard problems with confidence. 

Listen to this episode if you’re interested on answering the WHY to your unsolvable business problems using trustworthy and explainable AI.

Who is Casper Wilstrup?

Casper is the founder and CEO of Abzu®, the Danish/Spanish research startup that builds trustworthy AI to tackle high-risk challenges. Casper is the inventor of the QLattice® symbolic AI algorithm, an explainable AI that rationally reasons and makes evidence-based decisions. He has 20+ years of experience building large scale systems for data processing, analysis, and AI, and is passionate about the impact of AI on knowledge work and the intersection of AI with philosophy and ethics.

Check out our show notes for more info on Casper and Abzu.

Time Stamps

(00:00:00) Trailer

(00:01:22) About Casper and Abzu

(00:03:44) The history behind AI, the challenge of doing symbolic AI

(00:06:49) Why was Abzu formed?

(00:08:42) Duality of AI: symbolic and sub-symbolic AI

(00:13:05) How does symbolic reasoning work?

(00:14:44) How did Abzu solve the problem of symbolic AI? Meet QLattice

(00:18:43) Hypothetical scenario: Assessing legal cases (LLM vs. symbolic AI)

(00:23:13) Minimum requirements to properly run symbolic AI 

(00:24:42) Does symbolic AI need a lot of data to give a sound judgement?

(00:27:37) Is symbolic AI similar to causal inference? 

(00:33:53) Are there any limits to using symbolic AI? Explainable AI vs. Blackbox modelin AI

(00:38:30) Should people have prior knowledge on symbolic AI to use QLattice?

(00:41:15) Challenges and potentials of using symbolic AI

(00:43:41) Abzu’s business model; Is QLattice open-source?

(00:47:55) QLattice use case: medical research, life science and pharma

(00:51:50) Logistics use case: does QLattice work on-demand? 
(00:54:15) Closing remarks & book recommendation

--- More on G.M.S.C. Consulting

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Can we rely on LLMs to repurpose our content in social media?

To end our first season of the AI and Digital Transformation Podcast, we talked to dataroots R&D engineer Tim Leers about two very popular topics in 2023: LLMs, and content creation.

In this age of content creation and social media, journalists now have an extra role to fill: sharing their work and the news using their social media accounts. Given the popular use of ChatGPT and Midjourney, people ask LLMs to repurpose their news content for social media purposes.

This comes with a price. By relying solely on AI, journalists, like content creators, risk sharing repurposed content that are biased, polarizing and misinformed.

Listen to this episode and learn how you can make LLM more trustworthy when repurposing your existing content.

Who is Tim Leers?

Tim started his AI journey in neuroscience and psychology, studying the parallels between human & machine minds. 

Four years ago, he shifted his focus from brains to bytes, joining dataroots as an AI engineer, a leading company in AI and data-driven solutions. In this role, he assists organizations in the research, development, and deployment of cutting-edge AI systems. 

Tim is now primarily focused on how to effectively and responsibly utilize generative AI, agents, and LLMs, and advise decision-makers, engineers and end-users on how to navigate the expanding role of AI in work, life, and society.

Check out our show notes to know more about Tim, his work, and dataroots.

Time Stamps

(00:00:00) Trailer

(00:00:53) About Tim

(00:03:51) AI Use Case - Smart News Assistance

(00:05:48) Challenges in repurposing content using LLM

(00:07:52) LLM text-to-audio

(00:09:13) LLM workflow: Interactive process vs. automation 

(00:11:26) LLMs are not magic: summarizing & humans in the loop

(00:14:49) Journalist’s perception of AI: authenticity, trust and quality

(00:18:09) Is this the end of outsourcing a press agency for content?

(00:20:25) Search engine and algorithms: detecting unique news content 

(00:26:14) Risk of Conspiracies and Prompt Governance

(00:29:47) What makes dataroots’ smart news assistance tool different compared to ChatGPT?

(00:31:48) Do I need to finetune LLMs?

(00:34:18) Can Open source models replace ChatGPT?

(00:37:38) Adapting LLMs in businesses: Usability, APIs, Hardware vs. Cloud

(00:46:16) Future of Work, Critical thinking, LLM being a digital glue

(00:55:01) Recap, closing remarks and book recommendation

--- More on G.M.S.C. Consulting

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Do you struggle to keep your business afloat as you juggle between your client requests, complaints, and lead generation?

Small and medium businesses don’t have the financial resources to hire a bunch of call center agents or sales specialists to answer their clients’ queries or generate leads. 

AI can help you thrive and let you focus on what matters: making your products or services worth a great deal to your clients.

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, we talk with Oz Brown, Tech Solutions Specialist for Meet Smithers, an AI virtual assistant that can help you provide customer support by generating answers based on your business information. 

Listen to this episode and learn how AI conversational tools like Meet Smithers can help you attend to your clients’ needs while focusing on finishing their projects.

Who is Oz Brown?

Oz Brown has been in the technology scene for over a decade. His passions lie in software development, entrepreneurship, investing, and music. He’s currently working as a Tech Solutions Specialist for Answer Sales Calls Inc, an IT company that writes software for business’s marketing needs. He’s also one of the minds behind Meet Smithers, an AI call center that can assist any business in their customer support journey. With Meet Smithers, he hopes to empower solopreneurs in their wild crazy adventure of starting their own businesses. 

Check out our show notes to learn more about Oz and Meet Smithers.

Time Stamps

(00:00:00) Teaser

(00:00:54) About Oz Brown and Meet Smithers

(00:03:17) When do you use Meet Smithers?

(00:06:14) Do I need to have a big business to use Meet Smithers?

(00:09:51) Technology layer of Meet Smithers

(00:11:43) How does Meet Smithers operate during a call?

(00:15:17) Risks behind AI chatbots: LLMs, hallucination and trust issues

(00:17:24) What makes systems like Meet Smithers unique for small and medium businesses? Let’s talk about pricing.

(00:22:54) Is it better to have a person vs. AI as your virtual assistant?

(00:26:49) Do people realize that they are talking to a machine?

(00:29:50) Which kind of business scenarios can benefit from using Meet Smithers? Low ticket vs. High ticket solutions 

(00:38:48) Can AI persuade you to buy a product?

(00:40:57) How empathic is Meet Smithers during a conversation?

(00:42:40) Overview of possible impacts and influence of AI chatbots on businesses and economies

(00:46:39) Privacy protocol of Meet Smithers

(00:48:17) Re-thinking the way we approach jobs with AI: enterpreneurship 

(00:50:49) Does Meet Smithers analyze conversations?
(00:51:23) Closing remarks and book recommendations

--- More on G.M.S.C. Consulting

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How do you make your supply chain resilient to crisis and market changes?

Small or medium business owners have a hard time adapting to demand spikes or supply chain shortages.

AI can turn these challenges into opportunities and give your company a competitive advantage.  

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, we talk with Hugues Foltz, the Executive VP of Vooban, on how they helped a major Canadian stove family business thrive despite the pandemic and the Ukrainian war using AI. 

Listen to this episode and learn how to master your supply and demand using an AI-powered sales prediction tool.

Who is Hugues Foltz?

Hugues Foltz has been supporting companies in the development and execution of their digital transformation for more than 20 years. In 2018, he became co-owner and executive vice-president of Vooban, a firm recognized for its expertise in artificial intelligence and the development of custom software solutions. 

He is recognized for his ability to tackle business issues and challenges, which allows him to offer pragmatic innovation roadmaps that have significant impact. In addition to overseeing growth strategy and business development at Vooban, Hugues Foltz is involved in several organizations that revolve around Quebec’s technology and AI ecosystem

Check out our show notes to learn more about Hugues and Vooban.

Time Stamps:

(00:01:08) About Hugues Foltz and Vooban

(00:04:46) AI scene in Canada

(00:08:30) SBI sales prediction use case

(00:11:18) Sales accuracy: Algorithms vs. humans

(00:14:16) What does it take to train the machine to predict your sales? 

(00:16:31) Impact of sales prediction in a manufacturing business

(00:17:04) How do you measure accuracy in sales prediction? 

(00:18:58) Dealing with a black swan: Operating a supply-and-demand oriented AI during disruptive events i.e. pandemic, Ukrainian crisis and heating

(00:23:54) Why do we need applied AI scientists in solving clients’ problems?

(00:26:26) Should companies have AI champions to deal with people’s skepticism in AI?

(00:30:25) Are small and medium businesses lagging behind the digital transformation race? 

(00:39:05) Which is better: building bespoke AI solutions or no-code AI solutions?

(00:41:46) Should your business build an AI solution from scratch?

(00:44:12) Closing remarks and book recommendations

--- More on G.M.S.C. Consulting

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Saving money in business operations is an effective and consolidated application of machine learning and AI called predictive maintenance. 

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, together with Tom Pennington from VROC, we show you how predictive maintenance can lower both your warehouse cost and your asset's downtime, but only if good practices are followed.

Listen to our full episode, or check our chapter list below to make sure that you know everything you need to know to make your predictive maintenance project successful, with AI, of course.

Who is Tom Pennington?

For over 20 years, Tom has played a key role in completing the delivery of organizational and technology transformational strategies related to technology adoption, end-to-end automation, and new ways of working, with different companies across a wide range of technology sectors. 

He was also a founding member of the CBA CIO Innovation Board, which focuses on assessing and implementing emerging technologies to deliver transformational value to the group. 

At the moment, Tom Pennington is the Business Development Director for VROC, an AI company that offers clients transparency and autonomy when it comes to managing and optimizing their assets thanks to their platform and support. 

Check out our show notes to know more about Tom and VROC.

Chapters

(00:06:14) Teaser

(00:56:08) Intro to VROC and Tom Pennington

(05:35:14) Lifecycle of a predictive maintenance and optimization project 

(10:33:00) Data gathering, historical data,  and client's data awareness

(13:35:00) Working with engineers and other teams involved in the supply chain

(15:33:00) AI is not always a black box, the value of indirect data

(19:06:00) Knowledge transfer on data between VROC and clients on adapting AI

(21:43:20) When should a business opt for a customizable AI platform?

(26:21:22) The real value of the autoML platform, the importance of training and educating clients about the platform

(31:18:00) Integrating your business metrics into the AI platform by understanding the entire ecosystem, Root cause analysis

(36:25:15) Should business and technical people work together from the very beginning when starting an AI project?

(39:01:00) Ideal customer for VROC

(41:18:18) Tom's Book Suggestion

(42:06:22) Closing remarks

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There are some problems that just cannot be solved without the help of AI.

Like the one that we are discussing in today’s episode: Call Centers Monitoring and Optimization.

The problem is simple: 

• How do you know if your Customer Assistance call center is  solving the problems of your customers?

• And if it isn’t, how do you know what to improve ? 

Answering those simple questions in a traditional fashion would require some people from your company listening to hundreds of thousands of hours of conversation. It’s just crazy,  for anybody.

But there is a better solution.

In this episode of the AI and Digital Transformation Podcast, we are joined by Swati Sharma, lead data scientist at Brillio. We discuss how their AI solution, named Call Center Actionable Insights, solves this hard problem.

Who is Swati Sharma?

Swati Sharma earned her doctorate degree in electrical engineering and specialized on quantum physics and signal processing. She then pivoted her career to become a data scientist, with a strong analytical foundation. With over 15 years experience under her belt, she now leads Brillio’s data science team, engages with their stakeholders regarding their customized ML/NLP solutions, and teaches comprehensive post-graduate machine learning courses on the weekends.

We talk about speech to text, events modeling, root cause analysis insights and more. We will also go through some of the critical issues that need to be addressed for a successful deployment of a AI enabled  Customer Assistance monitoring and optimization solution.

Listen to this episode and find out how to make your customers happier and your business more profitable thanks to AI.

Check out our show notes to know more about Swati and Brillio.

Chapters:

[0:01:51] Introducing Swati and Brillio [0:04:01] The impact of a Mathematical background on the  Data Science Career

[0:05:41] The love for Data Science [0:07:36] How limiting is Plug and play AI? [0:10:11] Today’s use case: Call Center Actionable Insights (CCAI) [0:14:11] How CCAI solves the major issue of customer assistance monitoring and optimization. [0:18:41] Online vs offline calls monitoring [0:20:47] The timing and requirements of CCAI deployment  [0:23:03] The most wanted AI-generated customer assistance insights [0:25:42] How to act on the insights and make customer assistance better [0:28:11] Data requirements  and data privacy. [0:32:03] Law enforcement applications [0:33:51] Common issues and enablers of CCAI solution deployments [0:36:46] Does the workforce perceive AI as a threat? [0:40:01] Episode wrap up, references and Swati’s book suggestion.

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Music credits: storyblocks.com

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