Two ML researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain. Now to most people -- myself included -- "AI on the blockchain" sounds like a winning entry in some kind of startup buzzword bingo. But what I discovered talking to Jacob and Ala was that they actually have good reasons to combine those two ingredients together. At a high level, doing AI on a blockchain allows you to decentralize AI research and reward labs for building better models, and not for publishing papers in flashy journals with often biased reviewers. And that's not all -- as we'll see, Ala and Jacob are taking on some of the thorniest current problems in AI with their decentralized approach to machine learning.
Investment in fashion-related technology increased by 66% during the pandemic, according to research by The Business of Fashion and McKinsey. The report found that the value of the top 50 investments in fashion-related technology across the past year, either by fashion retailers or businesses that sell products and services to fashion-related companies, has increased by 66% to $16.2bn since 2019, indicating an increase of capital put into technology in the fashion sector. According to The Business of Fashion and McKinsey, around 55% of these investments went towards ecommerce technology, while the rest was mostly put into payments technology, buy-now-pay-later tech and social commerce. Investment in resale technology, supply chain and logistics management, non-fungible tokens, and virtual reality companies closely followed. Imran Amed, founder and CEO of The Business of Fashion, said: "The pandemic cemented technology's critical role in the fashion industry, particularly in terms of ecommerce adoption. But now the industry must lean even further into new technologies by experimenting in the metaverse, embedding fully digitised workflows across their organisations and investing in traceability tools to help them reach sustainability targets. Those who choose to wait on the sidelines risk being left behind."
FinTech as we know it now is highly specialized and centralized. Blockchain and AI can be catalysts for FinTech 2.0 focusing on holistic solutions with increased transaction speeds, transparency, and security. Furthermore, DeFi may mean a larger pool of investors as more and more people gain access to financial markets. The more investors there are, the more data there will be that would be impossible to process without AI. Blockchain provides the foundation for smart contracts to improve transparency and data management, while AI may be leveraged to scale processes, accelerate transactions, and extract insights from large volumes of data.
Besides cat videos, the one thing the internet surely needs more of is consultants talking about disruption. But as you read yet another post about the most overused (and misused) term in tech, I'd ask that you at least consider my argument and weigh in- especially if you disagree. Let's start with a few definitions. Clay Christensen, the author of disruption theory, first outlined his thesis of sustaining vs. disruptive technology in his 1995 Harvard Business Review article, and later in his classic The Innovator's Dilemma. In HBR he provides these definitions for sustaining vs. disruptive technologies: "Sustaining technologies tend to maintain a rate of improvement; that is, they give customers something more or better in the attributes they already value."
Artificial intelligence (AI) is all the rage now. It's impacting numerous industries globally and changing the way we do things. One of the critical industries AI is making strides in is the financial technology "fintech" industry. AI now plays a significant role in facilitating financial services, replacing what required manual work a few years ago. For example, banks now apply AI to assess credit risks with high accuracy.
The past few years have brought much hand wringing and arm waving about artificial intelligence (AI), as business people and technologists alike worry about the outsize decisioning power they believe these systems to have. As a data scientist, I am accustomed to being the voice of reason about the possibilities and limitations of AI. In this article I'll explain how companies can use blockchain technology for model development governance, a breakthrough to better understand AI, make the model development process auditable, and identify and assign accountability for AI decisioning. While there is widespread awareness about the need to govern AI, the discussion about how to do so is often nebulous, such as in "How to Build Accountability into Your AI" in Harvard Business Review: A healthy ecosystem for managing AI must include governance processes and structures.... Accountability for AI means looking for solid evidence of governance at the organizational level, including clear goals and objectives for the AI system; well-defined roles, responsibilities, and lines of authority; a multidisciplinary workforce capable of managing AI systems; a broad set of stakeholders; and risk-management processes. Additionally, it is vital to look for system-level governance elements, such as documented technical specifications of the particular AI system, compliance, and stakeholder access to system design and operation information.
In 2021, fashion companies invested between 1.6 and 1.8 percent of their revenues in technology. By 2030, that figure is expected to rise to between 3.0 and 3.5 percent. Behind the predicted increase is a conviction among many that technology could create a competitive edge--in customer-facing activities, where companies have mostly focused to date, and, more increasingly, in operations. Technologies such as robotics, advanced analytics, and in-store applications may help streamline processes and support sustainability, as well as create an exceptional customer experience (exhibit). This report is a collaborative effort by Imran Amed, Anita Balchandani, Achim Berg, Holger Harreis, Manuel Hurtado, Saga af Petersens, Roger Roberts, and Carlos Sanchez Altable, representing views from the Apparel, Fashion & Luxury Practice.
I hope that you enjoy the latest AI news and insights, don't forget to comment with your feedback. From this week you can find some interesting stuff added to the last section. But they have had a hard time shaking infighting and controversy over a variety of issues. Biased datasets are often the source for why AI models are also biased. "Adoption and scaling aren't things you add at the tail end of a project; they're where you need to start," Join 6000 aspiring Data Scientists to watch this FREE 75-minute session.