Category: AI

Democratizing Machine Learning to Predict the Future with Richard Harris of Black Crow AI


This post is by MPD from @MPD - Medium


On today’s pod I chat with Richard Harris, the Founder & CEO of Black Crow Ai.

Black Crow is a no-code, real-time machine-learning based predictive software that helps companies understand likely customer behavior.

Richard’s a veteran entrepreneur and has been involved in tech since the 90s. He cut his teeth in the world of consulting, was involved with Travelocity during the dot com boom, then has continued to be a serial founder.

As you’ll hear him explain, the world is turning into a browser. Between mobile devices, computers, wearable tech, self driving cars — real time data will be streaming from every part of our lives. He’s using this insight to build a company to help startups and brands collect and understand their first party data so that they can increase revenue and margins.

In addition to discussing how Blackcrow operates and the machine learning industry, we also talk about some strategies for how founders can navigate down market cycles — like the one we’re entering now. He’s been through three of these cycles and has some very helpful wisdom. If you started your company after 2010, then I definitely recommend that you give this one a listen.

Listen via your preferred platform here.

Show Links:

Please note, Interplay is an investor in Black Crow.


Democratizing Machine Learning to Predict the Future with Richard Harris of Black Crow AI was originally published in @MPD on Medium, where people are continuing the conversation by highlighting and responding to this story.

There’s no such thing as data



Technology is full of narratives, but one of the loudest is around something called ‘data’. AI is the future, and it’s all about data, and data is the future, and we should own it and maybe be paid for it, and countries need data strategies and data sovereignty. Data is the new oil!

This is mostly nonsense. There is no such thing as ‘data’, it isn’t worth anything, and it doesn’t really belong to you anyway.

Most obviously, ‘data’ is not one thing, but innumerable different collections of information, each of them specific to a particular application, that aren’t interchangeable. Siemens has wind turbine telemetry and Transport for London has ticket swipes, and you can’t use the turbine telemetry to plan a new bus route. If you gave both sets of data to Google or Tencent, that wouldn’t help them build a better image recognition system.

This might seem trivial put so bluntly, but it points to the uselessness of very common assertions, especially from people outside tech, on the lines of ’China has more data’ or ‘America will have more data’ - more of what data? Meituan delivers 50m restaurant orders a day, and that lets it build a more efficient routing algorithm, but you can’t use that for a missile guidance system. You might not even be able to use it to build restaurant delivery in London. ‘Data’ does not exist as one, single, unified thing, where you can add every row and table of every different kind (Read more...)

Industry 4.0: What Manufacturing Looks Like in the Digital Era



The following content is sponsored by ASE Global

ASE Industry 4.0 Manufacturing in the Digital Era Main

Industry 4.0: What Manufacturing Looks Like in the Digital Era

It might sound futuristic, but the Fourth Industrial Revolution—also known as Industry 4.0—has already begun.

Following the Industrial Revolution’s steam power, electrification in the 1800s, and the Digital Revolution of the late 20th century, Industry 4.0’s innovative smart technology is unlocking the next steps in automation.

So what does the next major evolution of manufacturing look like? This graphic from ASE Global breaks down the rollout of Industry 4.0, from increased robotization to lights-out manufacturing.

The Basics of Industry 4.0

Each industrial revolution has built on what came before, incorporating new technologies and knowledge of manufacturing. Industry 4.0 has four core principles paving the way:

  1. Interconnection: Machines, devices, sensors, and people in the manufacturing process all connecting and communicating with each other.
  2. Information transparency: Comprehensive data and information being collected from all points in the manufacturing process, allowing for more informed decisions.
  3. Technical assistance: Improved technological facility of systems assisting humans in decision-making, problem-solving, and difficult or unsafe tasks.
  4. Decentralized decisions: Cyber physical systems that are able to make decisions on their own and perform tasks autonomously.

Combining these principles is what makes the ongoing Fourth Industrial Revolution unique. Much of the underlying technology has been available for decades, including robotics and networks, but properly using them together unlocks a massive stride in manufacturing capabilities.

Already, the market size for Industry 4.0 specific technology was estimated to be $116.1 billion in 2021. (Read more...)

Creating the TikTok for STEM Education w/ Nhon Ma of Numderade


This post is by MPD from @MPD - Medium


Numerade is helping to change the world by digitizing educator’s knowledge using AI and short form videos. It’s an amazing company and Nhon is a great leader. Loved this interview.

Listen via your preferred platform here or watch below.


Creating the TikTok for STEM Education w/ Nhon Ma of Numderade was originally published in @MPD on Medium, where people are continuing the conversation by highlighting and responding to this story.

Cardiomatics bags $3.2M for its ECG-reading AI



Poland-based healthtech AI startup Cardiomatics has announced a $3.2M seed raise to expand use of its electrocardiogram (ECG) reading automation technology.

The round is led by Central and Eastern European VC Kaya, with Nina Capital, Nova Capital and Innovation Nest also participating.

The seed raise also includes a $1M non-equity grant from the Polish National Centre of Research and Development.

The 2017-founded startup sells a cloud tool to speed up diagnosis and drive efficiency for cardiologists, clinicians and other healthcare professionals to interpret ECGs — automating the detection and analyse of some 20 heart abnormalities and disorders with the software generating reports on scans in minutes, faster than a trained human specialist would be able to work.

Cardiomatics touts its tech as helping to democratize access to healthcare — saying the tool enables cardiologists to optimise their workflow so they can see and treat more patients. It also says it allows GPs and smaller practices to offer ECG analysis to patients without needing to refer them to specialist hospitals.

The AI tool has analyzed more than 3 million hours of ECG signals commercially to date, per the startup, which says its software is being used by more than 700 customers in 10+ countries, including Switzerland, Denmark, Germany and Poland.

The software is able to integrate with more than 25 ECG monitoring devices at this stage, and it touts offering a modern cloud software interface as a differentiator vs legacy medical software.

Asked how the accuracy of its AI’s ECG readings (Read more...)

Visualping raises $6M to make its website change monitoring service smarter



Visualping, a service that can help you monitor websites for changes like price drops or other updates, announced that it has raised a $6 million extension to the $2 million seed round it announced earlier this year. The round was led by Seattle-based FUSE Ventures, a relatively new firm with investors who spun out of Ignition Partners last year. Prior investors Mistral Venture Partners and N49P also participated.

The Vancouver-based company is part of the current Google for Startups Accelerator class in Canada. This program focuses on services that leverage AI and machine learning, and, while website monitoring may not seem like an obvious area where machine learning can add a lot of value, if you’ve ever used one of these services, you know that they can often unleash a plethora of false alerts. For the most part, after all, these tools simply look for something in a website’s underlying code to change and then trigger an alert based on that (and maybe some other parameters you’ve set).

Image Credits: Visualping

Earlier this week, Visualping launched its first machine learning-based tools to avoid just that. The company argues that it can eliminate up to 80% of false alerts by combining feedback from its more than 1.5 million users with its new ML algorithms. Thanks to this, Visualping can now learn the best configuration for how to monitor a site when users set up a new alert.

“Visualping has the hearts of over a million people across the (Read more...)

40 kilometers later


This post is by Om Malik from On my Om


Seven years ago, when traveling to Italy, I experienced the vagaries of data and its weird, unimaginative influence on our lives. Since then, the absurdity of what data-driven intelligence throws at us on a daily basis has increased exponentially. I wrote about it in an essay, 40 kilometers. It was part of a series of essays I wrote about data, its implications, and the emergence of limited-intelligence algorithms. If you are interested, here are some links to those articles in my archives.

Somehow that article, 40 kilometers, from seven years, ended up in the email inbox of my good friend Steve Crandall, who wrote a wonderful email reply in response. I thought it would be worth sharing and asked for his permission. Here it is:


The ‘data-driven world that we find all around us has little to do with science where data is highly contextualized and serendipity is welcomed and even hunted.  I think the notion of art is will be, or at least should be, important.

Operating as a simple person I like to make a distinction between awe and wonder. Both have multiple definitions, so I use my own.  Awe is a feeling of overwhelming majesty or even fear that seems to be beyond what we can understand or control. Wonder is a deep feeling of curiosity that leads to questions that can be addressed. (Read more...)

Didi gets hit by Chinese government, and Pelo raises $150M



Hello and welcome back to Equity, TechCrunch’s venture-capital-focused podcast where we unpack the numbers behind the headlines.

This is Equity Monday Tuesday, our weekly kickoff that tracks the latest private market news, talks about the coming week, digs into some recent funding rounds and mulls over a larger theme or narrative from the private markets. You can follow the show on Twitter here and myself here.

What a busy weekend we missed while mostly hearing distant explosions and hugging our dogs close. Here’s a sampling of what we tried to recap on the show:

It’s going to be (Read more...)

“Growth and scaling are the hardest things to get right” – Chainalysis’ Michael Gronager


This post is by Philippe Botteri from Cracking The Code



Earlier this year, Chainalysis announced its $100 million funding round, valuing the company at more than $2 billion and marking the fact that cryptocurrency is now mainstream. Since its Series C in November 2020, Chainalysis has increased ARR by more than 100% year-over-year, doubled its client base and now supports more than 100 digital assets across 10 native blockchains (around 90% of cryptocurrency economic activity). It’s been quite a journey getting here and there’s still a way to go as cryptocurrencies integrate with our global financial system.

I sat down (virtually) with Chainalysis co-founder and CEO Michael Gronager to discuss his learnings from almost seven years at the helm of a high-growth company. He shared his tips for building a global business, hiring, what he’d do differently and more…


The Chainalysis journey

PB: Let’s go back to the start. What was your career prior to founding first Kraken and then Chainalysis? What attracted you to entrepreneurship?

Before Kraken, I was planning and running public research infrastructure projects across Europe. Essentially, I was just doing politics and talking to research councils to get funding. I got to a point where I thought the purpose of the education and research systems we were creating was for people to build companies and that those who could do, should. After a conversation with a colleague, I decided to build something myself. I started coding iPhone apps and got one off the ground for a US company. This gave me some financial freedom, (Read more...)

Dabbel gets $4.4M to cut CO2 by automating HVAC for commercial buildings



Düsseldorf-based proptech startup Dabbel is using AI to drive energy efficiency savings in commercial buildings.

It’s developed cloud-based self-learning building management software that plugs into the existing building management systems (BMS) — taking over control of heating and cooling systems in a way that’s more dynamic than legacy systems based on fixed set-point resets.

Dabbel says its AI considers factors such as building orientation and thermal insulation, and reviews calibration decisions every five minutes — meaning it can respond dynamically to changes in outdoor and indoor conditions.

The 2018-founded startup claims this approach of layering AI-powered predictive modelling atop legacy BMS to power next-gen building automation is able to generate substantial energy savings — touting reductions in energy consumption of up to 40%.

“Every five minutes Dabbel reviews its decisions based on all available data,” explains CEO and co-founder, Abel Samaniego. “With each iteration, Dabbel improves or adapts and changes its decisions based on the current circumstances inside and outside the building. It does this by using cognitive artificial intelligence to drive a Model-Based Predictive Control (MPC) System… which can dynamically adjust all HVAC setpoints based on current/future conditions.”

In essence, the self-learning system predicts ahead of time the tweaks that are needed to adapt for future conditions — saving energy vs a pre-set BMS that would keep firing the boilers for longer.

The added carrot for commercial building owners (or tenants) is that Dabbel squeezes these energy savings without the need to rip and replace legacy systems — nor, (Read more...)