Emerson – Emerson Ventures Invests in EECOMOBILITY

EMR Analysis

More information on Emerson: See the full profile on EMR Executive Services

More information on Lal Karsanbhai (President and Chief Executive Officer, Emerson): See the full profile on EMR Executive Services

More information on Mike Baughman (Executive Vice President, Chief Financial Officer and Chief Accounting Officer, Emerson): See the full profile on EMR Executive Services

More information on Emerson Ventures: https://www.emerson.com/en-us/about-us/ventures + Emerson Ventures advances sustainable, emerging industrial technologies through venture capital investment.

Through its investments, Emerson Ventures accelerates innovation by obtaining early access to disruptive discrete automation solutions, environmental sustainability technologies and industrial software solutions that have the potential to solve real customer challenges.

More information on Thurston Cromwell (Vice President, Development and Innovation, Emerson – Head of Emerson Ventures, Emerson): See the full profile on EMR Executive Services

More information on NI (National Instruments) by Emerson: See the full profile on EMR Executive Services 

More information on Test & Measurement Segment by Emerson: See the full profile on EMR Executive Services

More information on Ritu Favre (Business Group President, Leader of the Test & Measurement Segment, Emerson): See the full profile on EMR Executive Services

 

More information on EECOMOBILITY (Strategic Investment by Emerson): https://eecomobility.ca/ + EECOMOBILITY is a startup from McMaster University’s Centre for Mechatronics and Hybrid Technology, specializing in Artificial Intelligence (AI). EECOMOBILITY has developed advanced testing and monitoring software tailored for the automotive, energy storage, industrial and manufacturing sectors. 

Their flagship product line, EECOPower, offers rapid battery cell and module testing and characterization systems designed for lithium-ion battery pack production. These systems are transformative for the automotive industry, capable of detecting defective batteries that can cause fires and performance issues. EECOPower utilizes advanced characterization techniques combined with AI, applicable to battery cells, modules and packs.

More information on Dr. Saeid Habibi (Founder and Chief Executive Officer, EECOMOBILITY): https://eecomobility.ca/our-team + https://www.linkedin.com/in/saeid-habibi-1917288/ 

 

More information on Automotive Ventures: https://www.automotiveventures.com/ + A global seed-stage mobility investor partnering with exceptional founders.

• 36 Portfolio Companies
• $250k Average Initial Check Size
• 25+ Years in Auto Industry
• 3 Automotive Ventures Funds

More information on Steve Greenfield (General Partner, Automotive Ventures): https://www.automotiveventures.com/team + https://www.linkedin.com/in/stevegreenfield416/

 

More information on RISC Capital: https://www.risc.capital/ + Deep Tech Venture Capital Fund

Investing in Canadian deep tech. Helping companies change the
world, one investment at a time. By engineers for engineers.

More information on Colin Webster (Co-founder, RISC Capital): https://www.risc.capital/ + https://www.linkedin.com/in/colin-webster-b094a51/

More information on Scott Pelton (Co-founder, RISC Capital): https://www.risc.capital/ + https://www.linkedin.com/in/scottpelton/ 

 

 

 

 

 

EMR Additional Notes:

• AI – Artificial Intelligence:
  • https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence  +
    • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
    • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but well a few, including Python, R and Java, are popular.
    • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
    • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
    • What are the 4 types of artificial intelligence?
      • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
      • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
      • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
      • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
    • Machine Learning (ML):
      • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
      • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
      • ML algorithms use historical data as input to predict new output values.
      • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
      • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
    • Deep Learning (DL):
      • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
      • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
      • DL is currently the most sophisticated AI architecture we have developed.
    • Computer Vision (CV):
      • Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.
      • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV):
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
      • Computer Vision systems can gain valuable information from images, videos, and other visuals, whereas Machine Vision systems rely on the image captured by the system’s camera. Another difference is that Computer Vision systems are commonly used to extract and use as much data as possible about an object.
    • Generative AI (GenAI):
      • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.

 

• Edge AI Technology:
  • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
  • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
  • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
• Multimodal Intelligence and Agents:
  • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
  • Multimodal capabilities allows to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
  • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
• Small Language Models (SLM) and Large Language Models (LLM):
  • Small language models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
  • LLM means large language model—a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
  • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.

 

• Software vs. Hardware vs. Firmware: 
  • Hardware is physical: It’s “real,” sometimes breaks, and eventually wears out.
    • Since hardware is part of the “real” world, it all eventually wears out. Being a physical thing, it’s also possible to break it, drown it, overheat it, and otherwise expose it to the elements.
    • Here are some examples of hardware:
      • Smartphone
      • Tablet
      • Laptop
      • Desktop computer
      • Printer
      • Flash drive
      • Router
  • Software is virtual: It can be copied, changed, and destroyed.
    • Software is everything about your computer that isn’t hardware.
    • Here are some examples of software:
      • Operating systems like Windows 11 or iOS
      • Web browsers
      • Antivirus tools
      • Adobe Photoshop
      • Mobile apps
  • Firmware is virtual: It’s software specifically designed for a piece of hardware
    • While not as common a term as hardware or software, firmware is everywhere—on your smartphone, your PC’s motherboard, your camera, your headphones, and even your TV remote control.
    • Firmware is just a special kind of software that serves a very narrow purpose for a piece of hardware. While you might install and uninstall software on your computer or smartphone on a regular basis, you might only rarely, if ever, update the firmware on a device, and you’d probably only do so if asked by the manufacturer, probably to fix a problem.

 

• OEM (Original Equipment Manufacturer):
  • Company that produces parts and equipment that may be marketed by another manufacturer.
  • Usually tagged on hardware or software that’s less expensive than normal retail products.
  • An OEM refers to something made specifically for the original product, while the aftermarket refers to equipment made by another company that a consumer may use as a replacement.
  • Electrical OEM manufacturers makes equipment or components that are then utilized by its customer, another manufacturer or a reseller, usually under the final reseller’s brand name. OEMs come in many shapes and sizes, making complete devices or specific components.
• MRO (Maintenance, Repair and Operations):
  • It refers to all the activities needed to keep a company’s facilities and production processes running smoothly.
  • Supplies consumed in the production process that do not become part of the end product.
  • Maintenance professionals use MRO items to maintain company structures, equipment, and assets. Purchases that fall under MRO include maintenance tools and equipment, replacement parts for production equipment, consumables such as personal protective equipment (e.g., safety goggles, work gloves), cleaning products and office supplies.
• Integrated Supply:
  • Integrated supply chain management refers to an enterprise resource planning approach to supply chain management.
  • Large-scale business strategy that brings as many links of the chain as possible into a closer working relationship with each other. The goal is to improve response time, production time, and reduce costs and waste.
  • Often takes the form of integrated computer systems. For example, the supplier’s computer system may be set up to deliver real-time data to the buyer’s computer. This allows the buyer to know: The current status of all orders., which products are in the supplier’s inventory …
  • Integration, operations, purchasing and distribution are the four elements of the supply chain that work together to establish a path to competition that is both cost-effective and competitive.
  • Integrated supply is the end-to-end process of managing the MRO supply chain (spare parts) through consolidated sourcing practices, storeroom operations, inventory management, data governance, and continuous improvement. The objective is to leverage spend, reduce transactions, and cut inventory and associated costs while eliminating risk around critical spares.