WARC (April 13)
“Search marketing is on the cusp of its most consequential transformation since Google first introduced its sponsored keyword search auction over 20 years ago, and the more recent introduction of the use of data and algorithms to provide greater personalisation in search results.” As we move into the Search 3.0 era, it will be “defined as much by image or video as text, and by artificial intelligence and natural language processing, in which marketers shift from targeting keywords to targeting intent and context.”
Tags: AI, Algorithms, Auction, Consequential, Data, Google, Image, Intent, Keywords, Marketing, Natural language processing, Personalisation, Search 3.0, Transformation, Video
Financial Times (July 23)
“Computer algorithms encoded with human values will increasingly determine the jobs we land, the romantic matches we make, the bank loans we receive and the people we kill, intentionally with military drones or accidentally with self-driving cars.” The way those human values are embedded “will be one of the most important forces shaping our century. Yet no one has agreed what those values should be” and the “debate now risks becoming entangled in geo-technological rivalry between the US and China.”
Tags: Algorithms, China, Computer, Debate, Drones, Encoded, Geo-technological rivalry, Human values, Jobs, Loans, Self-driving cars, U.S.
Institutional Investor (December 14)
It’s difficult for analysts to rise above groupthink since they “tend to use the same quantitative information…and similar methodologies.” Remarkably, some do. Institutional Investor recognizes them annually. Still, it seems that one day algorithms will “displace analysts entirely, meaning that these algorithms will themselves become eligible, in principle, for election to the II All-America Research Team.”
Tags: Algorithms, All-America Research Team, Analysts, Groupthink, Information, Methodologies, Quantitative
Institutional Investor (May 30)
Firms “are doubling down on machine learning and other quantitative investing efforts.” More advanced than rule-based algorithms, “with machine learning, a computer sifts through billions of data points, picking up patterns. Armed with this knowledge, it learns trading behaviors such as buying dips or selling high over time, based on what it has gleaned about the market from past and present data.” Despite the inroads, however, human ingenuity remains essential.
Tags: Algorithms, Computer, Data points, Dip, Firms, Ingenuity, Investing, Machine learning, Market, Patterns, Quantitative, Trading
The Economist (March 12)
“Now after five decades, the end of Moore’s law is in sight.” This might not prove a bad thing as the quest for improvement will turn to more promising areas, such as the “deep learning” technology that recently beat Go legend Lee Sedol. “Huge performance gains can be achieved through new algorithms. Indeed, slowing progress in hardware will provide stronger incentives to develop cleverer software.”
Tags: Algorithms, Deep learning, Gains, Go, Hardware, Improvement, Moore’s Law, Performance, Progress, Promising, Software, Technology
