Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
Differences Between AI and Machine Learning and Why it Matters
Data Driven Investor | Roberto Iriondo | Oct 15, 2019
Why do tech companies tend to use AI and ML interchangeably?
Unfortunately, some tech organizations are deceiving customers by proclaiming using AI on their technologies while not being clear about their products’ limits
The term “artificial intelligence” came to inception in 1956 by a group of researchers including Allen Newell and Herbert A. Simon [9], AI’s industry has gone through many fluctuations. In the early decades, there was a lot of hype surrounding the industry, and many scientists concurred that human-level AI was just around the corner. However, undelivered assertions caused a general disenchantment with the industry along the public and led to the AI winter, a period where funding and interest in the field subsided considerably.
Afterwards, organizations attempted to separate themselves with the term AI, which had become synonymous with unsubstantiated hype, and utilized different terms to refer to their work. For instance, IBM described Deep Blue as a supercomputer and explicitly stated that it did not use artificial intelligence [10], while it actually did.
See:
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The Age of Artificial Intelligence in Fintech
-
How Data-driven Strategies Can Improve Impact Investing Outcomes
During this period, a variety of other terms such as big data, predictive analytics and machine learning started gaining traction and popularity. In 2012, machine learning, deep learning and neural networks made great strides and started being utilized in a growing number of fields. Organizations suddenly started to use the terms machine learning and deep learning to advertise their products.
Deep learning started to perform tasks that were impossible to do with classic rule-based programming. Fields such as speech and face recognition, image classification and natural language processing, which were at early stages, suddenly took great leaps.
Hence, to the momentum, we are seeing a gearshift back to AI. For those who had been used to the limits of old-fashioned software, the effects of deep learning almost seemed like “magic” [16], especially since a fraction of the fields that neural networks and deep learning are entering were considered off limits for computers. Machine learning and deep learning engineers are earning skyward salaries [11], even when they are working at non-profit organizations, which speaks to how hot the field is.
All these elements have helped reignite the excitement and hype surrounding artificial intelligence. Therefore, many organizations find it more profitable to use the vague term AI, which has a lot of baggage and exudes a mystic aura, instead of being more specific about what kind of technologies they employ. This helps them oversell, redesign or re-market their products’ capabilities without being clear about its limits.
In the meantime, the “advanced artificial intelligence” that these organizations claim to use, is usually a variant of machine learning or some other known technology.
Sadly, this is something that tech publications often report without profound examination, and frequently go along AI articles with pictures of crystal balls, and other supernatural portrayals. Such deception helps those companies generate hype around their offerings. Yet, down the road, as they fail to meet the expectations, these organizations are forced to hire humans to make up for the shortcomings of their so-called AI [12]. In the end, they might end up causing mistrust in the field and trigger another AI winter for the sake of short-term gains.
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November 10, 2026 | NCFA Fintech Market Insight | Capital Markets Infrastructure And Funding, Entrepreneurs Startups Small Businesses, Equity Crowdfunding And Alternative Funding A small group of large rounds is carrying Canadian venture totals while deal count and growth stage financing remain well below recent norms. On November 10, 2026, the Canadian Venture Capital and Private Equity Association published its Q3 2026 venture capital report. Canadian companies raised $4.3 billion across 397 deals through September, only 1% less capital than a year earlier. But 23 rounds of $50 million or more represented just 6% of disclosed deals and absorbed 58% of all venture capital invested. Deal count has also fallen every year since 2021. The national total is holding up far better than the number of companies getting financed. Six Big Rounds Carried 59% Of Q3 Venture Capital Q3 produced $1.35 billion across 129 deals. Capital was 1% higher than Q2, but deal count fell 15%. Six rounds above $50 million accounted for $793 million, or 59% of the quarter’s capital. That's a decent quarter for the companies that closed those rounds, but a weak quarter for everyone else. Mega rounds above $50 million totalled $2.5 billion across 23 transactions ...
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November 10, 2026 | NCFA Fintech Market Insight | Digital Assets Blockchain And Tokenization, Capital Markets Infrastructure And Funding, Regulation And Policy Funds, Collateral And Settlement Lead Canada’s Near Term Tokenization Opportunity On November 10, 2026, the Canadian Securities Administrators published its Project Tokenization Phase 1 findings, setting out where Canadian market participants see the strongest commercial opportunities for tokenization and what regulators plan to test next. The report draws on 233 organizations, 21 bilateral meetings and 80 survey responses, along with workshops attended by 281 people in Calgary and 491 in Toronto. Participants put investment funds, private assets, collateral, treasury and settlement infrastructure ahead of public stocks as the strongest near term opportunities. The report also cuts through one of tokenization’s biggest sales pitches. Blockchain can change how an asset is recorded, transferred or settled, but it doesn't automatically create liquidity, investor demand, broader eligibility or stronger ownership rights. Those outcomes still depend on the product, market, legal structure and infrastructure around it. The CSA now plans to take specific projects into Phase 2 through a 120 day public call for proposals. Firms may be able to use existing regulatory processes, tailored regulatory treatment or bounded testing in a ...
Nov 11, 2026 A positive work environment is key for any team that wants to succeed. Team morale is crucial for productivity, motivation, and job satisfaction. Engaging in activities that lift spirits and strengthen connections can lead to great results. Creating a positive team environment requires creativity, engagement, and some fun. Each activity can strengthen connections, boost motivation, and improve workplace dynamics. When teams focus on strategies to boost morale, they work toward shared success. These activities can lead to lasting memories and help build a stronger team culture. Here are some fun strategies to help improve your team's morale and make the workplace livelier. Fun at the Arcade Visiting an arcade spot is a great way to enjoy time together and build teamwork. Imagine your team facing challenges with various games. Whether it's racing or playing team games, the arcade encourages friendly competition and bonding. These fun experiences can help reduce stress and improve relationships among coworkers. Arcades offer many options to suit diverse interests and skill levels. The lively atmosphere invites everyone to relax and take a break from their daily tasks. Shared laughter and enjoyment can lead to better communication and teamwork when you return to the ...
November 10, 2026 | Market Activity | Fundraising, Investing, Trading, Leadership, Competition, Human Capital, Health A Brokerage Race Beyond $0 Commissions On November 10, 2026, Webull Canada added Nasdaq Basic Canada, giving clients free real time Level 1 quote and trade data across Canadian listed equities. The feed provides best bid, best offer and last sale information for stocks listed on the TSX, TSX Venture, CSE and Cboe Canada. Nasdaq says its consolidated feed can reduce Level 1 market data costs by more than 60% compared with traditional Canadian data packages. Webull isn't first to give Canadian investors free real time information. The more interesting story is how quickly another paid feature is becoming part of the basic brokerage account. Wealthsimple helped normalize $0 trading in 2019, Questrade followed in 2025 and Webull dropped its own commissions in April 2026. With the headline trading fee largely gone, brokers now need other reasons for investors to move money and keep it on the platform. Who Led Canada’s Race To $0 Trading And Free Market Data Wealthsimple Trade launched nationally on March 13, 2019 with $0 commissions on Canadian and U.S. stocks and ETFs. Questrade removed commissions on listed stocks and ETFs ...
November 10, 2026 | Market Activity | Artificial Intelligence And Data, Capital Markets Infrastructure And Funding A Toronto Fintech Riskfuel Analytics Brings AI Pricing Into BBVA’s Global Markets Workflow On November 10, 2026, BBVA deployed Riskfuel Analytics' neural pricing technology inside the pre-trade structuring workflow for autocallable products on its ePricer platform. Riskfuel is a Toronto fintech founded in 2019 that builds AI models for derivatives pricing. BBVA is a global bank with a large corporate and investment banking business. The deployment puts Canadian built AI directly into the calculations used to design complex structured products at a major global bank. Riskfuel's model is trained on BBVA's own pricing engine and uses more than 4,000 market and product inputs. The company says the surrogate was tested against more than 100,000 trades and can evaluate thousands of candidate structures in under a minute. BBVA had already added AI generated investment ideas to ePricer in September. Riskfuel goes further into the workflow by speeding up the numerical pricing work while BBVA's own model remains the reference. How Riskfuel Speeds Up BBVA's Autocallable Pricing Autocallables can depend on volatility, interest rates, dividends, maturity, coupon levels and several barrier conditions. Autocallables are structured investments ...
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