Applied AI Pod

Episode highlights:

• 01:00 - Conversational AI for the future of marketing and sales, focus on the real estate industry.
• 04:00 - How Structurely works and what it solves.
• 06:50 - Benefits to businesses utilizing AI within their companies.
• 10:55 - The future of real estate by use of machine learning.
• 16:10 - Creating a more promising future for AI as a tool for positive outcomes. E.g. Zillow.
• 23:00 - Conversational AI's next big challenges.

References:

• Nate's LinkedIn profile
• Nate's Twitter profile
• Structurely's Company Website

• 02:00 - Ada's performance, stories and metrics around. Size of the impact AI has in this space, as covered by Tradeshift.
• 05:35 - Working with AI/ML teams.
• 14:40 - Assessing how much data is needed for an AI project.
• 18:45 - Data risks.
• 24:25 - Is Agile good for AI teams?
• 27:30 - How much does UX matter in e-Invoicing and ML/Data projects?
• 36:35 - How can projects get derailed or fail? What should we watch out for.
• 40:05 - Funny fails.
• 41:50 - AI principles.

References:

• Lloyd's Linkedin Profile
• Tradeshift's Ada technology
• Tradeshift's surpass of $1 trillion in transactions processed on their platform.

• 02:35 - Why hasn’t voice AI taken off already?
• 22:50 - Can we fulfil an end to end new purchase naturally?
• 32:20 - How can we resolve the disambiguation problem in NLU?
• 37:20 - Context and memory perspectives.
• 43:20 - How do we make conversations natural?

References:

• Dustin's VUX World Podcast
• Dustin's Linkedin profile
• Hannes' LinkedIn profile
• Speechly's Twitter profile
• Speechly product search and checkout demo
• Speechly's Interspeech Research Paper 2021

• 01:15 - How does NLP work?
• 04:05 - How do Transformer-based NLP models work?
• 08:20 - How to look at unstructured data to take advantage of it more.
• 12:00 - How to leverage ML to bring more to unstructured data?
• 15:25 - Approach for low resources languages.
• 23:25 - Word embeddings for common reasoning needs.
• 26:55 - Techniques to follow to improve error and ambiguity in training data or for a model in general.
• 30:10 - Are GPTs leading effort in the field in a wrong direction?
• 34:15 -  Is DeepLearning the end of AI?
• 37:20 - What are some good NLP metrics to watch?
• 42:05 - How do we get past transactional queries to conversational queries?
• 52:00 - Is the Turing test still relevant for NLP or has it become obsolete?

References:

• AI-Powered Search referenced in respect of text not being unstructured.
• Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
• Rethinking Search:Making Experts out of Dilettantes Common sense reasoning
• TWIML AI podcast 518 with Yejin Choi
• DARPA's Explainable AI Project
• EPITA is an engineering school in Paris.
• Marc's LinkedIn profile.

• 12:50 - Is the Turing test still relevant?
• 21:30 - Why it's important to use methodologies in AI projects and what are some best practices out there fit for AI projects.
• 28:00 - Falsehoods of methodologies in AI projects.
• 35:00 - Is Agile a good framework for AI/ML projects/products?
• 40:10 - How can projects get derailed or fail if you don't have a plan in place.
• 44:20 - The best compliment one can get after building an AI project or system.
• 47:25 - Is DL the end of AI?

References:

• CPMAI methodology
• Cognilytica's Voice Assistant Benchmark 1.0 and 2.0
• AI Today podcast show with Alexandra Petrus as guest
• AI Today podcast show

• 2:10 - Using AI to augment and reshape creativity in a modern world. Psychological creativity and story creativity - can an AI model help AI music artists, today, get off their creative blocks?
• 12:15 - Attempt to define ‘good’ music, using a cognitive music literature background.
• 17:00 - Are we better or worse off, for AI in audio/music? Is it sustainable for the effort input and cost, impact and efficiency output?
• 22:35 - ‘Deep Nostalgia” from myheritage initiative, and GPT-J - looking for strengths in the two approaches.
• 29:25 - The Sound of AI community - a HuggingFace version for audio?
• 31:15 - Train a DL - CNN sound classifier built with Pytorch and torchaudio on the Urban Sound 8k dataset.
• 35:00 - Is deep learning a dead end for artificial intelligence?
• 38:05 - Could someone that is a pure tech profile ever be in such an intersection in sync with the artistic world? Is it a pre-req to be domain savvy to build AI audio solutions?
• 42:10 - Helping music tech companies with a focus on audio (voice, speech, sound), the experience so far.
• 49:45 - Hard problems to solve when dealing with AI audio - Top three.
• 56:50 - First piece of music composed by a machine.

References:

• The Sound of AI YT Channel: https://www.youtube.com/c/ValerioVelardoTheSoundofAI/featured
• Sign up for The Sound of AI Slack Community
• PyTorch for Audio + Music Processing https://www.youtube.com/watch?v=gp2wZqDoJ1Y&list=PL-wATfeyAMNoirN4idjev6aRu8ISZYVWm
• Audio Signal Processng for ML https://www.youtube.com/watch?v=iCwMQJnKk2c&list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0
• OpenSource Research project building a speech-operated neural synthesiser
• Deep Learning for Music https://github.com/ybayle/awesome-deep-learning-music
• Sweet Anticipation book: Music and the Psychology of Expectation by David Huron
• Valerio Velardo's LinkedIn
• The Frame Problem of AI

• 1:50 - Using AI for the environment
• 6:55 - AI spices for agriculture
• 12:15 - AI in outdoor uses
• 15:15 - Green AI in Seekar's work
• 22:15 - Training AI models for a green AI approach
• 27:10 - Seekar in the medical space, and covid19 opportunities
• 39:15 - NLP tradeoffs and takeaways
• 43:10 - Similarities in practicing jiu-jitsu and AI

References:

• Building AI models to be greener, and Seekar's Research Gate paper. This paper gives more insight into how Seekar was able to compress a large AI model down to a small enough size without compromising accuracy or performance.
• COVID-AI app from AppStore
• Exeda (Exploratory Emotional Detection Agent), mentioned in reference of using NLP for emotion recognition. Seekar's goal is to develop a psychological screening tool that can be downloaded as an app and used to check mental health daily through a 30-second voice recording in a similar manner as one brushes their teeth daily. 80% of personal communication happens through body language and Seekar’s products are utilizing this principle to better treat mental health. Research paper in progress.

• 01:25 - Do NLP models need someone that is not completely monolingual?
• 05:20 - Types of NLP  in marketing and/or e-commerce.
• 11:30 - Challenges in the e-commerce space: Behavioural data gathered by cookies has disappeared.
• 16:00 - Every 40 seconds, our attention breaks. Is that fact taken into account in NLP modeling for personalization?
• 18:20 - Models like GPT-3 open a whole new commercialization avenue in the marketing world, specifically for content creation. Impact of the wave.
• 21:50 - Is it fair to use an AI model for IP and content in such a way you influence millions of users on a website at once?
• 30:45 - Explainable models, debugging and how models could function.
• 37:00 - Provocative contexts for data scientists nowadays.
• 41:00 - Future of NLP.

Episode references:

• GPT3 the beginning of a new app ecosystem
• Amazon makes Alexa Conversations generally available to developers
• Copy.AI and Taglines.AI based on GPT3. Other spinoffs in the same space: Copy Shark; Snazzy AI; experiments using platforms like VWO.
• Explainable models by DARPA
• NLP in Marketing, part 1
• How virtual assistants (i.e. in your smartphone) understand you
• AI and NLP in marketing, webinar
• Katherine's Linkedin
• Katherine's Twitter
• Bucharest AI's meetup on Gender Imbalance, AI Mentorship & good delivery in AI

• 01:35 - Why did you decide to continue bootstrapping and decided to not opt for an investment.
• 06:50 - In the age of the million dollar supremacy how much money is a VC ready to invest.
• 08:56 Open source AI, good or bad idea? - VC and deep tech founder perspective.
• 14:15 - What’s the ideal shareholder split?
• 20:40 - Should one opt for Europe instead of Silicon Valley to raise capital faster?
• 23:10 - Effects of the pandemic on the deep tech investment space.
• 29:10 - Do VCs run their due diligence in their investment process + should VCs start considering checking reddit channels from now on?
• 32:45 - The gap between early stage deep tech startups and investments.
• 41:30 - Time, as an essential factor, in a deep tech startup  - time from idea to prototype.
• 49:45 - How is a founder coping with the long development cycle from a cost / business model perspective.
• 55:00 - Pre-seed to seed stage, where is the role of AI/ML: core, feature, end-to-end, black box.
• 59:10 - How much is reusing vs. proprietary AI work.
• 01:01:15 - What does a VC scout do?

Reference links:

Alexander Piskunov's LinkedIn

Amandine Flachs' LinkedIn

Amandine Flachs' Twitter

Venture Capital Scout Programs

• 02:43 - Motivation behind building a scaled MOOC AI course
• 06:40 - Effort behind an AI course to educate 1% of EU citizens
• 10:45 - Finland's heritage in education, and AI takeaways for course takers
• 20:55 - AI Challenge, or how are companies joining the AI education movement
• 25:45 - Digital spending priority: digital skills & education OR upgrading our health systems - Opinion
• 30:13 - Feels of a creator after building a popular AI course
• 36:55 - Ethics of AI course, and Elements of AI new chapters exploration

References:

• Elements of AI Romania
• Elements of AI global version 
• EU local Elements of AI Partners & movement
• Ethics of AI course
• Ready AI
• Finnish Center for AI
• Prof. Teemu Roos LinkedIn
• Prof. Teemu Roos Twitter
• Artificial Intelligence from Finland e-book