Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. A cryptocurrency is a digital currency that may be traded without the involvement of a government or bank. On the other hand, cryptocurrencies are generated using cryptographic processes that allow users to purchase, sell, and exchange them safely.
Blockchain is the new talk of the town. It is the technology behind cryptocurrencies like Bitcoin. Today, it has turned out to be a game-changer for businesses. Its decentralized ledger offers transparency and immutability in transactions between parties without any intermediary. The transactions are irreversible, which means once a ledger is updated, it can never be changed or deleted. Blockchain technology will eventually find its space in the new and innovative applications of Machine Learning and Artificial Intelligence.
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.
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.
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.