Suspense, plot and character have long been considered the foundations of a good thriller – but really, the sign of a mystery done well is a sound. It's the "hwwwwahhh!" of air whooshing past your lips and down into your lungs after a pivotal reveal. It's a noise certain to be heard during the hours spent untangling the secret at the heart of Telling Lies. Sam Barlow's critically acclaimed Her Story (2015) introduced us to the concept of the interactive search engine thriller. In that game, you play an unnamed character who, for reasons unknown, is sitting at a computer trawling through hours of stolen police station video footage showing a suspect being interviewed.
For more than half a century our digital search engines have relied upon the humble keyword. Yet over the past few years, search engines of all kinds have increasingly turned to deep learning-powered categorization and recommendation algorithms to augment and slowly replace the traditional keyword search. Behavioral and interest-based personalization has further eroded the impact of keyword searches, meaning that if ten people all search for the same thing, they may all get different results. As search engines depreciate traditional raw "search" in favor of AI-assisted navigation, the concept of informational access is being harmed and our digital world is being redefined by the limitations of today's AI. At first glance, the evolution of search from simple TF-IDF keyword queries into today's AI-powered personalized digital navigation is a positive step towards making the digital world more accessible to the general public.
In this episode Steve Zakur and I are curious about the ways AI can be used to drive greater value for your company. We have our opinions about our own software of course, but this is a bigger question: how can you use AI to make your entire team and business smarter? What companies need to think about right now is AI augmentation -- augmenting decision making. Sometimes we're thinking way too big about AI, instead of in a targeted fashion about what it can do for us now. This is the importance of practical AI.
I really think the internet of everything or as we all call the IoT (Internet of Things), continues to surround us by smart devices and smart systems that are constantly sensing, monitoring, listening, and watching everything we do. Many of these systems are also constantly learning from what they sense, see, and hear in their environment, as well as from the feedback they receive from other smart devices and systems. This opens the door to some really helpful insights in life and business. Because so many enterprises recognize the value of data in today's connected world, data centers are a growing sector of the technology space. What can we expect in terms of data in the coming years?
As technology advances to meet new data demands, it also creates new areas of opportunity for business growth and operational efficiency. Today, for example, some technologies already enable automatic query optimization, while machine learning algorithms help automate a variety of once-manual functions. Advances in technology are even starting to let data warehouses tune themselves. This capability is accelerating the speed at which data warehouses deliver value to businesses. With the advance of machine learning and availability of near-infinite storage and computing power in the cloud, we're headed toward an exciting new era: the age of the self-adapting data warehouse.
Managing stakeholders in the world of data science projects is a tricky prospect. I have seen a lot of executives and professionals get swept up in the hype around data science without properly understanding what a full-blown project entails. And I don't say this lightly – my career has been at the very cusp of machine learning and delivery. I hold a Ph.D. in Data Science and Machine Learning from one of the best institutions in the world and have several years of experience working with some of the top industry research labs. I moved to Yodlee, a FinTech organization, in 2016 to run the data sciences product delivery division.
To organize, find, and evaluate machine leaning model experiments, use Amazon SageMaker model tracking capabilities. Developing models typically requires extensive experimenting with different datasets, algorithms, and parameter values. Using the model tracking capability, you can search, filter and sort through hundreds and possibly thousands of experiments using model attributes such as parameters, metrics and tags. This helps you find the best model for your use case quickly. Find, organize, or evaluate training jobs using properties, hyperparameters, performance metrics, or any other metadata.
When we think SEO, Google is the next thought most of the time, right? Pleasing the "Google Gods" gets trickier as technology evolves. The art of staying visible on the web is always changing, and unless you're an SEO specialist, it can be tough to stay on top of the ever-evolving trends. There is so much global competition. And web user attention spans are perilously short.
Contractors and major construction companies are each trying to achieve broadly the same business objectives, like streamlining projects, mitigating risks (financial and human), saving time and costs, and ensuring that planning and project engineering are accurate. The tools many use can be very different, irrespective of role in the process. It's a surprising fact that even some globe-straddling multinational construction conglomerates run most of their daily operations on Microsoft Excel, given the well-publicized inherent inaccuracy of that particular platform. Is it also surprising to find a tiny contracting company in a niche area of construction running a cutting-edge SaaS to ensure its operations are run smoothly? Project management, engineering, and construction in general involve huge numbers of variables that make ensuring efficient levels of productivity and progress extraordinarily tricky.
Curious to know what the psychology of avoiding lions on the savannah has in common with responsible AI leadership and the challenges of designing data warehouses? Decision intelligence is a new academic discipline concerned with all aspects of selecting between options. It brings together the best of applied data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them. It's a vital science for the AI era, covering the skills needed to lead AI projects responsibly and design objectives, metrics, and safety-nets for automation at scale. Decision intelligence is the discipline of turning information into better actions at any scale.