The document discusses Defined Capital's activities in Q3 2023, which included mapping out the AI revolution and meeting with startups to inform their investment strategy. Their thesis is to invest in startups using data and AI to solve problems in new ways compared to traditional software. The rest of the document discusses the state of AI adoption, opportunities, challenges, and predictions for 2024, which include multi-agent models becoming prominent and most enterprise software embedding generative AI, among other predictions.
Patrick Couch - Intelligenta Maskiner & Smartare Tjänster
Industriföretag, såväl tillverkare som användare av maskiner, fordon och utrustning, står inför ett paradigmskifte drivet av ökad global konkurrens, kunders förändrade efterfrågan samt det faktum att produkterna nu blir instrumenterade, ihopkopplade och mer intelligenta. Stora datamängder är inte ett buzzword för dessa företag, utan en reell verklighet som de behöver förhålla sig till för att säkra sin framtida verksamhet. I bästa fall omvandlar dessa företag denna teknologiska revolution (populärt kallad Internet of Things, Industrial Internet, M2M, Industri 4.0 etc.) till en motor för att utveckla verksamheten mot tillväxt och effektivare produktion. Detta skifte skapar framförallt stora möjligheter att förflytta sig mot leveranser av tjänster som kraftigt ökar mervärdet för kunderna, deras kunders kunder samt för producenten.
Artificial Intelligence: Competitive Edge for Business Solutions & Applications
The growth of Artificial Intelligence in recent years brought forth a major challenge for brands in deploying such AI solutions. Many brands lack the clarity regarding where to start the AI integration process and profitably deploy these solutions in the most effective manner.
This document provides a summary of key strategies for successfully scaling artificial intelligence (AI) within an organization. It discusses the importance of having a clear business strategy that AI supports, focusing AI projects on delivering tangible business value. It also emphasizes having the right data strategy to power AI initiatives and taking a portfolio view of AI projects that balances experimentation with alignment to strategic goals. The document recommends challenging assumptions about how work gets done and preparing employees for how AI will change and augment their roles. It argues that organizations must think holistically about scaling AI to realize its full potential for driving business outcomes.
Mining intelligent insights with ease: AI/ML for Financial Services
This year, the focus goes beyond technology to mining business insights around how cloud enables strategic industry trends such as Open and Virtual Banking and Insurance, Security and Compliance, Data Analytics and AI/ ML, FinTech and RegTech, Surveillance and more through sharing of best practices and use cases. In sessions led by customers, partners, industry leaders and AWS subject matter experts, you’ll learn how AWS helps financial institutions to focus on the innovation and outcomes that truly drive business forward. Business stakeholders, market makers, and technology owners will all learn something new, valuable and actionable.
AI for Enterprises-The Value Paradigm By Venkat Subramanian VP Marketing at B...
AI is here, call it buzz, cause it a bubble, we are smack in the middle of an AI revolution. While there is a strong view building about consumer AI applications, there still seems to be some scepticism about AI for enterprises, primarily due to the lack of clarity and focus on how AI can actually deliver value for enterprises. At BRIDGEi2i, we believe it is important to have a non-fragmented view of the AI ecosystem and a “Value Roadmap” for AI in the enterprise context. As CxOs, it is important to understand where the enterprise is in the transformation journey and define value accordingly. This talk will throw light on how to look at the enterprise AI ecosystem and build the right roadmap for value.
Evaluating the opportunity for embedded ai in data productivity tools
The document discusses embedding AI in data productivity tools. It notes that while AI applications get attention, opportunities to embed AI in tools to manage and interpret data may be overlooked. This could help address challenges in preparing large amounts of clean, AI-ready data at scale. The document summarizes experts' views, many of which support embedding AI in data pipelines and tools to improve data quality, integration and analytics. It provides examples of companies like Informatica, UnifiSoftware, Trifacta and Tamr that are infusing AI into their data integration and catalog offerings. The document argues that opportunities for AI to improve data management could boost overall AI success rates.
Ai - Artificial Intelligence predictions-2018-report - PWC
Here’s some actionable advice on artificial intelligence (AI), that you can
use today: If someone says they know exactly what AI will look like and
do in 10 years, smile politely, then change the subject or walk away.
AI in Media & Entertainment: Starting the Journey to Value
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Moving to the Cloud: Artificial Intelligence in Cloud-Based Solutions
With the advent of chatbots, artificial intelligence, interactive voice response, and machine learning, novel technologies continue to disrupt the contact center industry. These advances initially gave the impression that automation will replace the human element. Join Rick Nucci, Co-Founder and CEO of Guru, as he demystifies AI, explains how machine learning helps contact centers rather than replaces them, and demonstrates how to leverage this new technology to create innovative solutions.
Moving to the Cloud: Artificial Intelligence in Cloud-Based Solutions
With the advent of chatbots, artificial intelligence, interactive voice response, and machine learning, novel technologies continue to disrupt the contact center industry. These advances initially gave the impression that automation will replace the human element. Join Rick Nucci, Co-Founder and CEO of Guru, as he demystifies AI, explains how machine learning helps contact centers rather than replaces them, and demonstrates how to leverage this new technology to create innovative solutions.
AI in Manufacturing: moving AI from Idea to Execution
#AI and #HPC convergence is here and is here to stay and accelerate innovations across industries. The increased availability of data, hardware advancements leading to increased computational capabilities, and new algorithms and mathematical models have collectively resulted in the accelerated AI expansion in all sorts of applications. This, however, creates high computational needs which naturally have been more and more successfully addressed by HPC (High-Performance Computing). In that sense, AI & HPC complement each other. HPC infrastructure is often used to train AI’s powerful algorithms by leveraging huge amounts of sample data (training set) and in that way enables AI models (trained algorithms) to recognize shapes, objects (machine vision), find answers hidden in the data (predictive maintenance, data analytics) or accelerate time to results (predict the outcome of complex engineering simulations).
We at byteLAKE have been closely working with Lenovo, Lenovo Infrastructure Solutions Group, Intel Corporation, NVIDIA and many more to ensure that our AI-powered products not only help our clients efficiently automate various operations and reduce time and cost but also are highly optimized and make the most of the hardware and software infrastructure where they are deployed. Besides our efforts in bringing AI solutions to the paper industry and manufacturing in general (which I described in my previous post), our efforts in bringing value thru AI in the chemical industry highly benefit from HPC's capabilities to dynamically scale and keep up with performance requirements. Our product, #CFDSuite (AI-accelerated CFD) leverages HPC to efficiently analyze historic CFD simulations and makes it possible for our clients to predict their outcomes on various edge devices i.e. laptops, desktop PCs or local edge servers. And with that in mind, I am very happy to see the byteLAKE team becoming one of the drivers of AI & HPC convergence and leveraging it to bring innovations to various industries.
Links:
- byteLAKE's Cognitive Services: www.byteLAKE.com/en/CognitiveServices (Cognitive Services (AI for Paper Industry & Manufacturing)). Related blog post series: www.byteLAKE.com/en/CognitiveServices-toc
- byteLAKE's CFD Suite: www.byteLAKE.com/en/CFDSuite. Related blog post series: www.byteLAKE.com/en/AI4CFD-toc
- byteLAKE’s CFD Suite (AI-accelerated CFD) — HPC scalability report: https://marcrojek.medium.com/bytelakes-cfd-suite-ai-accelerated-cfd-hpc-scalability-report-25f9786e6123 (full report: https://www.slideshare.net/byteLAKE/bytelakes-cfd-suite-aiaccelerated-cfd-hpc-scalability-report-april21)
- byteLAKE's CFD Suite (AI-accelerated CFD) - product community: www.bytelake.com/en/AI4CFD-pt2 (LinkedIn and Facebook groups)
#AI #IoT #Manufacturing #Automotive #Paper #PaperIndustry #ChemicalIndustry #CFD #FluidDynamics #OpenFOAM #ArtificialIntelligence #DeepLearning #MachineLearning #ComputerVision #Automation #Industry40
Our latest research reveals the need for companies to complement their technology advances with a focus on governance that drives ethics and trust. Otherwise, their AI efforts will fall short of competitors’ initiatives that responsibly embrace machine intelligence.
How Companies Can Move AI from Labs to the Business CoreCognizant
APAC and Middle East organisations have big expectations from AI, but they’re only just getting started. To realise the full potential of AI-led innovation, they must rapidly, but smartly, scale their deployments and embrace a strong ethical foundation, keeping a close eye on the human implications and cultural changes required to convert machine intelligence from lofty concept to business reality.
Artificial intelligence (AI) offers new opportunities to radically reinvent the way we do business. This study explores how CEOs and top decision makers around the world are responding to the transformative potential of AI.
Patrick Couch - Intelligenta Maskiner & Smartare Tjänster IBM Sverige
Industriföretag, såväl tillverkare som användare av maskiner, fordon och utrustning, står inför ett paradigmskifte drivet av ökad global konkurrens, kunders förändrade efterfrågan samt det faktum att produkterna nu blir instrumenterade, ihopkopplade och mer intelligenta. Stora datamängder är inte ett buzzword för dessa företag, utan en reell verklighet som de behöver förhålla sig till för att säkra sin framtida verksamhet. I bästa fall omvandlar dessa företag denna teknologiska revolution (populärt kallad Internet of Things, Industrial Internet, M2M, Industri 4.0 etc.) till en motor för att utveckla verksamheten mot tillväxt och effektivare produktion. Detta skifte skapar framförallt stora möjligheter att förflytta sig mot leveranser av tjänster som kraftigt ökar mervärdet för kunderna, deras kunders kunder samt för producenten.
Artificial Intelligence: Competitive Edge for Business Solutions & Applications9 series
The growth of Artificial Intelligence in recent years brought forth a major challenge for brands in deploying such AI solutions. Many brands lack the clarity regarding where to start the AI integration process and profitably deploy these solutions in the most effective manner.
This document provides a summary of key strategies for successfully scaling artificial intelligence (AI) within an organization. It discusses the importance of having a clear business strategy that AI supports, focusing AI projects on delivering tangible business value. It also emphasizes having the right data strategy to power AI initiatives and taking a portfolio view of AI projects that balances experimentation with alignment to strategic goals. The document recommends challenging assumptions about how work gets done and preparing employees for how AI will change and augment their roles. It argues that organizations must think holistically about scaling AI to realize its full potential for driving business outcomes.
Mining intelligent insights with ease: AI/ML for Financial ServicesAmazon Web Services
This year, the focus goes beyond technology to mining business insights around how cloud enables strategic industry trends such as Open and Virtual Banking and Insurance, Security and Compliance, Data Analytics and AI/ ML, FinTech and RegTech, Surveillance and more through sharing of best practices and use cases. In sessions led by customers, partners, industry leaders and AWS subject matter experts, you’ll learn how AWS helps financial institutions to focus on the innovation and outcomes that truly drive business forward. Business stakeholders, market makers, and technology owners will all learn something new, valuable and actionable.
AI for Enterprises-The Value Paradigm By Venkat Subramanian VP Marketing at B...Analytics India Magazine
AI is here, call it buzz, cause it a bubble, we are smack in the middle of an AI revolution. While there is a strong view building about consumer AI applications, there still seems to be some scepticism about AI for enterprises, primarily due to the lack of clarity and focus on how AI can actually deliver value for enterprises. At BRIDGEi2i, we believe it is important to have a non-fragmented view of the AI ecosystem and a “Value Roadmap” for AI in the enterprise context. As CxOs, it is important to understand where the enterprise is in the transformation journey and define value accordingly. This talk will throw light on how to look at the enterprise AI ecosystem and build the right roadmap for value.
Evaluating the opportunity for embedded ai in data productivity toolsNeil Raden
The document discusses embedding AI in data productivity tools. It notes that while AI applications get attention, opportunities to embed AI in tools to manage and interpret data may be overlooked. This could help address challenges in preparing large amounts of clean, AI-ready data at scale. The document summarizes experts' views, many of which support embedding AI in data pipelines and tools to improve data quality, integration and analytics. It provides examples of companies like Informatica, UnifiSoftware, Trifacta and Tamr that are infusing AI into their data integration and catalog offerings. The document argues that opportunities for AI to improve data management could boost overall AI success rates.
Ai - Artificial Intelligence predictions-2018-report - PWCRick Bouter
Here’s some actionable advice on artificial intelligence (AI), that you can
use today: If someone says they know exactly what AI will look like and
do in 10 years, smile politely, then change the subject or walk away.
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Moving to the Cloud: Artificial Intelligence in Cloud-Based SolutionsAggregage
With the advent of chatbots, artificial intelligence, interactive voice response, and machine learning, novel technologies continue to disrupt the contact center industry. These advances initially gave the impression that automation will replace the human element. Join Rick Nucci, Co-Founder and CEO of Guru, as he demystifies AI, explains how machine learning helps contact centers rather than replaces them, and demonstrates how to leverage this new technology to create innovative solutions.
Moving to the Cloud: Artificial Intelligence in Cloud-Based SolutionsNicolas Rodriguez
With the advent of chatbots, artificial intelligence, interactive voice response, and machine learning, novel technologies continue to disrupt the contact center industry. These advances initially gave the impression that automation will replace the human element. Join Rick Nucci, Co-Founder and CEO of Guru, as he demystifies AI, explains how machine learning helps contact centers rather than replaces them, and demonstrates how to leverage this new technology to create innovative solutions.
AI in Manufacturing: moving AI from Idea to ExecutionbyteLAKE
#AI and #HPC convergence is here and is here to stay and accelerate innovations across industries. The increased availability of data, hardware advancements leading to increased computational capabilities, and new algorithms and mathematical models have collectively resulted in the accelerated AI expansion in all sorts of applications. This, however, creates high computational needs which naturally have been more and more successfully addressed by HPC (High-Performance Computing). In that sense, AI & HPC complement each other. HPC infrastructure is often used to train AI’s powerful algorithms by leveraging huge amounts of sample data (training set) and in that way enables AI models (trained algorithms) to recognize shapes, objects (machine vision), find answers hidden in the data (predictive maintenance, data analytics) or accelerate time to results (predict the outcome of complex engineering simulations).
We at byteLAKE have been closely working with Lenovo, Lenovo Infrastructure Solutions Group, Intel Corporation, NVIDIA and many more to ensure that our AI-powered products not only help our clients efficiently automate various operations and reduce time and cost but also are highly optimized and make the most of the hardware and software infrastructure where they are deployed. Besides our efforts in bringing AI solutions to the paper industry and manufacturing in general (which I described in my previous post), our efforts in bringing value thru AI in the chemical industry highly benefit from HPC's capabilities to dynamically scale and keep up with performance requirements. Our product, #CFDSuite (AI-accelerated CFD) leverages HPC to efficiently analyze historic CFD simulations and makes it possible for our clients to predict their outcomes on various edge devices i.e. laptops, desktop PCs or local edge servers. And with that in mind, I am very happy to see the byteLAKE team becoming one of the drivers of AI & HPC convergence and leveraging it to bring innovations to various industries.
Links:
- byteLAKE's Cognitive Services: www.byteLAKE.com/en/CognitiveServices (Cognitive Services (AI for Paper Industry & Manufacturing)). Related blog post series: www.byteLAKE.com/en/CognitiveServices-toc
- byteLAKE's CFD Suite: www.byteLAKE.com/en/CFDSuite. Related blog post series: www.byteLAKE.com/en/AI4CFD-toc
- byteLAKE’s CFD Suite (AI-accelerated CFD) — HPC scalability report: https://marcrojek.medium.com/bytelakes-cfd-suite-ai-accelerated-cfd-hpc-scalability-report-25f9786e6123 (full report: https://www.slideshare.net/byteLAKE/bytelakes-cfd-suite-aiaccelerated-cfd-hpc-scalability-report-april21)
- byteLAKE's CFD Suite (AI-accelerated CFD) - product community: www.bytelake.com/en/AI4CFD-pt2 (LinkedIn and Facebook groups)
#AI #IoT #Manufacturing #Automotive #Paper #PaperIndustry #ChemicalIndustry #CFD #FluidDynamics #OpenFOAM #ArtificialIntelligence #DeepLearning #MachineLearning #ComputerVision #Automation #Industry40
Our latest research reveals the need for companies to complement their technology advances with a focus on governance that drives ethics and trust. Otherwise, their AI efforts will fall short of competitors’ initiatives that responsibly embrace machine intelligence.
Each CIO post description includes something resembling the 12 roles and requirements. This list outlines what CEOs are currently looking for in their CIOs. However, it's not necessarily what CEOs really need from their CIOs.
In the current data-driven economy, in which analytics and software have become the main factors in business, executives must reconsider the hierarchies and silos that fueled the business in the past. There is no longer a need for "technology people" who work independently of "data people" who work in isolation from "sales" people or from "finance." Instead, they need to manage organizations where every employee is embraced by technology and data as integral to their work.
They also require CIOs to guide them there. In this regard, redefining the business to accommodate the new data economy is the primary task executives have to today's top-of-the-line CIOs.
Here's how:
from Software and the Business to Software is the Business
When Cargill began to put IoT sensors in shrimp ponds, Chief Information Officer Justin Kershaw realised that the $130 billion agriculture business was evolving into a digital enterprise. To determine the point at which IT should stop and where IoT technology engineering needs to begin, Kershaw did not call CIOs from other food and agricultural companies to discuss their experiences. He contacted the CIOs of SAP and Microsoft as well as various other companies that use software. He was thinking about reimagining the world's biggest agricultural business as a software business.
Modern Delivery
Moving software from a supporting role to leading position is the why is the issue, then modern delivery is the way to do it. Modern delivery involves an approach to product (rather as project) management rapid development and small teams of cross-functional experts which co-create, as well as continuous integration and delivery, all with a brand new financial model that supports "value" not "projects."
However, don't try to build an modern SDLC. Instead, build a software development cycle (SDLC) on an industrial infrastructure. The architecture that is intended for this data-driven economy relies on platforms and cloud-connected, makes use of APIs that connect with an ecosystem outside and splits monolithic applications into microservices.
"A platforms model encompasses more than just an architecture. It's a mental model that allows us to consider how vertically we can provide the vet, farmer, or pet's owners, then expand to think horizontally about ways to make solutions adaptable, scalable and secure" claims Wafaa Mamilli Chief Information Officer and Digital Officer of global animal health firm Zoetis. "Platforms can be flexible, intelligent and run algorithms that let us rapidly change. If we did not adopt the platform model and approach, we'd be funding these massive programs."
The Democratisation of IT
If you gift someone an uncooked fish, they can take a bite for a few hours.
Learn more about what senior insurance executives and employees are thinking and doing with regard to artificial intelligence. Read how roles and tasks are likely to change as people start to work more collaboratively with intelligent machines, and find out what the key steps are to developing the insurance workforce of the future.
RPA is growing rapidly, with the market expected to increase 57% over the next year. UiPath's chief evangelist Guy Kirkwood outlines six predictions for 2019: 1) Attended robots that work with humans will surpass unattended robots, flipping the ratio from 30:70 to 70:30. 2) RPA adoption in the public sector will "explode" to do more with less and increase citizen services. 3) Business cases will focus on improved employee engagement rather than headcount reductions. 4) Outsourcing will decline drastically as organizations automate internally. 5) AI adoption will slowly enter the mainstream within 12-18 months. 6) Analyst growth projections will continue to underestimate the actual growth
Get Ready: AI Is Grown Up and Ready for BusinessCognizant
Despite great enthusiasm for AI, full-blown deployments remain the exception rather than the rule across businesses in the U.S. and Europe, according to our recent research. Businesses can turn the tide by honing their AI strategies, maintaining a human-centric approach, developing governance structures and ensuring AI applications are built on an ethical foundation.
Similar to Defined Capital - Mapping the AI Revolution and State of Adoption.pdf (20)
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
What's Next Web Development Trends to Watch.pdfSeasiaInfotech2
Explore the latest advancements and upcoming innovations in web development with our guide to the trends shaping the future of digital experiences. Read our article today for more information.
In this follow-up session on knowledge and prompt engineering, we will explore structured prompting, chain of thought prompting, iterative prompting, prompt optimization, emotional language prompts, and the inclusion of user signals and industry-specific data to enhance LLM performance.
Join EIS Founder & CEO Seth Earley and special guest Nick Usborne, Copywriter, Trainer, and Speaker, as they delve into these methodologies to improve AI-driven knowledge processes for employees and customers alike.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
How to Avoid Learning the Linux-Kernel Memory ModelScyllaDB
The Linux-kernel memory model (LKMM) is a powerful tool for developing highly concurrent Linux-kernel code, but it also has a steep learning curve. Wouldn't it be great to get most of LKMM's benefits without the learning curve?
This talk will describe how to do exactly that by using the standard Linux-kernel APIs (locking, reference counting, RCU) along with a simple rules of thumb, thus gaining most of LKMM's power with less learning. And the full LKMM is always there when you need it!
UiPath Community Day Kraków: Devs4Devs ConferenceUiPathCommunity
We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/07/intels-approach-to-operationalizing-ai-in-the-manufacturing-sector-a-presentation-from-intel/
Tara Thimmanaik, AI Systems and Solutions Architect at Intel, presents the “Intel’s Approach to Operationalizing AI in the Manufacturing Sector,” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge is powering a revolution in industrial IoT, from real-time processing and analytics that drive greater efficiency and learning to predictive maintenance. Intel is focused on developing tools and assets to help domain experts operationalize AI-based solutions in their fields of expertise.
In this talk, Thimmanaik explains how Intel’s software platforms simplify labor-intensive data upload, labeling, training, model optimization and retraining tasks. She shows how domain experts can quickly build vision models for a wide range of processes—detecting defective parts on a production line, reducing downtime on the factory floor, automating inventory management and other digitization and automation projects. And she introduces Intel-provided edge computing assets that empower faster localized insights and decisions, improving labor productivity through easy-to-use AI tools that democratize AI.
2. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
We spent Q3 2023 mapping out the AI revolution to target our theses
and investments alongside engaging with a multitude of startups and
companies to inform our strategy and investment decisions.
Our thesis: to invest in startups solving well-defined problems with the
power of data and AI in ways that traditional software has not been
able to accomplish yet.
Defined is led by a proven VC and former operator and engineer with
15+ years of company building and investing in data, automation and AI.
2
Overview
3. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
2
Humans + Machines Relationship Inversion
1
It used to be to that humans had to communicate with machines in programming languages
designed for machines to comprehend – now they take input in our language and meet use
where we are at.
GenAI and NLP represent not just an AI revolution, but a profound inversion in the relationship
between humans and machines
It used to be that humans would create and computers would validate results – now the
machines create and humans QA.
3
Specialized and technically advantaged pick-and-shovel building blocks benefiting from
arms race
3
With new LLM capabilities, we see applications that go beyond what humans have been
capable of alone – look no further than machine-driven breakthrough in the natural sciences
like AlphaFold and GNoMe – and the emergence of systems of intelligence.
This is setting the stage for a profound shift in how products provide value and AI is adopted
into workflows
4. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
To Achieve Adoption, Need to Embrace “Four A’s” of Human Enablement with
AI to complete “Jobs to be Done”
4
I no longer need to do this
Job To be Done (JBTD)
because of AI.
AI helps automate low-
level, repetitive tasks
like debugging, lead
gen and outbound
emailing.
AI can help me do this
JTBD better.
More human-centric,
higher ROI work like
customer service,
strategy and
campaigns.
Automation
(tasks)
Augmentation
(capabilities)
Alignment
AI helps the whole company
do their jobs better
High potential to coordinate
with teams, department,
business units and wide
company towards shared
JBTDs, outcomes and goals
(KPIs, OKRs).
Adoption
Value of AI can only be
unlocked if can overcome
barriers to adoption of
humans and companies.
5. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Key Criteria to Evaluate Attractiveness of JTBDs for AI
5
• A top pain point in many industries is workforce retention, especially among
workers who require significant upfront and ongoing training. These are areas
where hiring “AI staff members” (versus buying software and mandating
employees to change their workflow to use it) has high potential for uptake.
1
2
3
4
• Areas in which humans are prone to error or are generally slow and inefficient
(even when supported by software products) are most likely to benefit from AI
approaches.
• Areas of higher, more specialized labor spend, or areas in which an AI product
could simply ride existing revenue or transaction rails–either for substitute
human services or software tools.
Area of high
spend on highly
trained labor
Potential 10x
performance
with AI
Areas with low
adoption of
software
Established revenue
rails and financial
incentives
• Enterprises are more likely to adopt AI if its cost benefit is at least an order of
magnitude (and ideally more!) better than the status quo. Therefore, we’re likely to
see a stronger opportunity in areas that have a low penetration of existing
software tools, where AI cost benefit is being compared to human labor, versus
software.
6. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Adoption Rate of Disruptive New Technologies
After its first decade, the cloud reach 30% of enterprise software spend; the internet 45% penetration;
and mobile nearly 85%, the pace of AI adoption will be dramatically faster.
Source: Menlo Ventures
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1990 1995 2000 2005 2010 2015 2020 2025
Internet Smartphones Cloud AI
US Technology Adoption %
6
7. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL 7
Comparison: Autonomous Vehicles vs. Generative AI
Gen AI advancing much faster than previous technological waves.
Levels of Autonomy Autonomous Vehicles Generative AI
L5
L4
L3
L2
L1
Fully autonomous
Highly autonomous
Self-driving with light
intervention
Tesla autopilot
Cruise control
Superhuman reasoning & perception
AI autopilots for complex tasks
AI co-pilot for skilled labor
Supporting humans with basic tasks
Generating basic content
15
Years
5
Years
Source: Coatue
8. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL 8
Magnitude
Wave 1: AI Natives – Bard, Character, Midjourney, OpenAI
Wave 2.0: Early startup wave - Harvey, Perplexity, Langchain
Wave 2.1: Fast mid-market companies - Notion, Zapier
Wave 3 (Pending): Next startup wave - Showing sustainable value
Wave 4 (Pending): REAL enterprise adoption - BIG WAVE
Time
TODAY
AI Adoption Curves
True enterprise adoption is
still many quarters/years
away.
Given that large enterprise
planning cycles often take
3-6 months, and then
prototyping and building will
take a year for a large
company, we are still very
far away from peak AI usage
or peak AI hype.
Source: Elad Gill
9. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Supervised Learning
(Labelling Things)
Generative AI
Reinforcement
Learning
Value from AI Technology Today → 3 Years
Supervised learning is massive majority of AI deployment, and Andrew Ng predicts it should double in the next 3
years. Generative AI should more than double, but it won't catch up in terms of scale.
Don't let online hype lead you astray. Learning the fundamentals is as important as it's always been.
Rather than view LLMs, Transformers, and diffusion models as part of a continuum with past "AI", it is worth
thinking of this as an entirely new era and discontinuity from the past
Unsupervised
Learning
9
Source: Andrew Ng, Stanford
10. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
AI Spend
10
Enterprise investment in GenAI - which is estimated to be $2.5B in 2023 is surprisingly small compared to
the enterprise budgets for traditional AI ($70B) and cloud software ($400B).
$400B
$70B
Cloud software spend Total AI Spend Gen. AI Spend
AI spend has potential to grow by
up to 6x in the next 7 years to
match current Cloud spend
$3B
Source: Menlo Ventures
11. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Potential Annual Value of AI and Analytics Across Industries
11
CLICK HERE FOR INTERACTIVE CHART
$9.5T - $15.4T
Focusing investments where the most significant unlocks in value and market adoption will materialize
Source: MckInsey
12. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
State of AI Adoption – Where are we today?
12
• A majority (77.1%) of survey
respondents said that their
companies have made
some sort of effort to adopt
AI.
• But around half (48.9%)
said those efforts were
fledgling—just getting
started or ad-hoc use
cases.
• A non-trivial 15.7% haven’t
really started yet, and might
not anytime soon.
`
15.7% 29.6% 19.3% 14.8% 13.4%
We haven’t
started
adopting yet
We’re getting
the basics in
place
We have
some ad-hoc
use cases in
production
We have
several use
cases in
production
We’re leading
the industry in
AI adoption
It is early days for AI in most organizations with experimentation before production.
Company’s Level of AI Adoption
Source: Retool
13. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
The State of AI Adoption - Where are we today?
Data Is Key, But It’s Not Ready
• CDOs believe data is key to preparing for generative AI, but they haven’t done much with it yet.
• 93% agree that “data strategy is crucial to getting business value from generative AI.”
• However, 57% said that they had made no changes to data yet to prepare for generative AI.
• Only 38% agreed that “My team and I have the right data foundation to pivot to generative AI,” and only
11% agreed strongly with the statement.
• 71% agreed that “generative AI is interesting, but we are more focused on other data initiatives to
achieve more tangible value.” Tangible value is great, but perhaps this low priority is why many CDOs
haven’t been given responsibility for generative AI.
• At least they are planning to spend more on the technology: 62% said that their teams are planning on
investing more in generative AI.
13
Source: Menlo Ventures
14. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Slow adoption at first and widespread adoption in second half of decade.
14
0%
10%
20%
30%
40%
50%
60%
70%
80%
F500 CEO Survey: % expectation of AI impact on headcount
Lower Labour Need Unchanged Labour Need
Lower Labour Need. Unchanged Labour Need.
Next Year Next 5 Years
Executive
Leadership
Senior
Management
Engineering
Sales
& Support
Growth
& Marketing
Operations
Finance
& Legal
Leadership
Product & Engineering
Sales, Support
& Marketing
Finance
& Legal
AI as a co-pilot or autopilot could transform how organizations scale for growth
→ Previously meant scaling headcount, with AI means scaling compute
Today Near Future
Product
The State of AI Adoption - Where are we going?
Source: Goldman Sachs, KPMG, Gartner, Coatue
16. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Market Assessment
In long-term value will accrue in unexpected ways as realized during significant technological shifts historically.
In medium-term, rapid adoption of AI is a baseline, not differentiator for healthy growth, and it’s easy to forget that network effects (human, data,
brand, trust, distribution, etc.) and effective GTM serve as key differentiators between lasting winners and losers.
→ Likely majority of value created in 2-3 years after a platform disruption – Uber, Airbnb, and Instagram all created <3 years of the iPhone launch
AI advances (i.e. OpenAI Dev Day and Github Universe) are causing weekly disruption across the entire knowledge stack, from content creation
and code generation to intelligent decision-making systems, unlocking massive opportunities for growth and innovation on a scale that surpasses
previous AI milestones.
→ The result: reduced barriers to entry across the board for businesses and uncertainty on where value will truly accrue long term
Form factor is evolving. GenAI apps are now going beyond "first draft + human review" to increased autonomy to solve end to end (0 to level 5
autonomy). Midjourney’s introduction of camera panning and infilling is a nice illustration of how the generative AI-first user experience is evolving
with a new set of knobs and switches that are very different from traditional editing workflows – advancing from zero-shot to ask-and-adjust.
→ Form factors are evolving from individual to system-level productivity and from human-in-the-loop to execution-oriented agentic systems.
There is still an expectation vs. reality gap. Generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value.
User engagement is lackluster. Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps
have a median of 14%. This means that users are not finding enough value in Generative AI products to use them every day yet.
→ To build enduring business, need to fix retention and generate deep enough value for customers that they stick and become daily active users
Despite challenges GenAI has already had a more successful start than SaaS, with >$1 billion in revenue from startups alone (it took the SaaS
market years, not months, to reach the same scale). Hype and flash are giving way to real value and whole product experiences. A shared playbook is
developing as companies figure out the path to enduring value. We now have shared techniques to make models useful, as well as emerging UI
paradigms that will shape generative AI’s second act.
Business models for AI are emerging to sell work, not software. We are seeing startups differentiating their business model compared to incumbents
by instead of selling software on a per seat basis to selling units of work our outcomes based on a user consumption basis. Selling work opens new
vertical opportunities that wouldn’t have otherwise supported a software company.
16
17. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Is the GenAI bubble about to burst?
• In GenAI, we are experiencing Amara’s Law -- the phenomenon that we tend to overestimate the effect of a technology in the
short run and underestimate the effect in the long run or as futurist Paul Saffo says:
• “Never mistake a clear view for a short distance”
• While the technology holds profound promise, we see early signs that GenAI may get a “cold shower” in 2024 as the costs,
risks and complexity associated with the technology reach a tipping point.
• The hype of 2023 has ignored several obstacles that will slow progress in the short term. The cost of deployment is a
prohibitive factor for many organizations and developers. Additionally, future regulation and the social and commercial risks
of deploying generative AI in certain scenarios result in a period of evaluation prior to roll-out.
• We are therefore applying patience and judgment in our investment decisions, with careful attention to how founders are
solving the value problem.
• This prediction hold even more weight in light of several other recent developments:
• AI relies on chips to run, and there are serious concerns about a growing global chip shortage.
• The computing power necessary to keep large language models running is tremendous — not to mention the
environmental impact.
• AI startups seem to be facing increasing pressures too, with AI speech recognition startup Deepgram recently cutting
staff and AI marketing startup Jasper slashing revenue projections.
• Generative AI deals are also down per Pitchbook, with both deals and deal value slowing in the third quarter of 2023.
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18. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
2024 Predictions
The next big phase of AI will be multi-agent models. Soon, we’ll enter a world where you might just be interacting with one model on the surface, yet that model can
search for and leverage many unique models “under the hood”.
→ Will see emergence of Large Behavior, Action, Vision Models (LBMs, LAMs and LVMs) and potentially other modalities.
Despite GenAI having a cold shower in 2024, almost all enterprise software companies will embed generative AI in at least some of their products in 2024.
Powerful pre-trained open-source models will dominate in the enterprise, with only a few (or maybe one) giant foundation model companies serving consumers
→ Models will go to the data lakes, not the other way around
EU AI Act is delayed and redrawn multiple times owing to the speed of AI advancement which makes the construction of a robust and workable regulatory
framework extremely difficult. There are differences of opinion between the US, EU and market participants, with Europe taking a far more structured and robust
approach to regulation. Legislation is not finalized until late 2024, leaving the industry to take the initial steps at self-regulation.
AI oversight committees become commonplace in large organizations by 2024. Companies establish diverse oversight committees composed of AI ethics experts,
legal advisors, data scientists and representatives to review applications of AI in the business, set guidelines, conduct audits and address ethical and legal concerns.
SLMs (Small Language Models) are likely to become a force to be reckoned with as LLMs keep pushing the scaling laws and become bigger and bigger, whereas the
SLM thesis centers around the viability of smaller, highly specialized, more affordable models for specific use cases (movement has partly been catalyzed by the rise
of open-source GenAI models)
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