Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
The Delta variant of the coronavirus spread to more countries in recent weeks, and the total number of cases officially logged soared past half a million per day. The global number of deaths is now about two-thirds as high as it was at the peak of the previous wave, in April of this year. As the virus spreads, the potential rises for a vaccine-resistant strain to emerge. Meanwhile, in poorer countries, vaccines are scarce, and most populations are little protected (exhibit).
Ipsotek was selected by Sydney Trains to deliver an AI-based video analytics solution to security cameras to identify incidences of tunnel and track intrusion at 13 stations across metropolitan Sydney. "Sydney Trains chose Ipsotek, after an extensive comparison of a number of products," said Mark Edmonds, manager of security capability for transport at Sydney Trains. "Ipsotek's proven track record in the AI video analytics space, its partnership with Genetec and its work with Innovate UK's Small Business Research Initiative (SBRI) for railway-focused AI applications, made it a well-deserved choice." Ipsotek's project with Sydney Trains follows the company's previous success in delivering an initial programme of work via the Innovate UK SBRI initiative, for the development of AI video analytics, to enhance the rail experience for passengers and staff in the UK. "As a British SME, competing against some of the industry's big-named companies, we are delighted to have been chosen by Sydney Trains, to deliver this project," said Chris Bishop, sales director APAC & marketing director at Ipsotek.
Advanced analytics and other AI-driven tools and technologies have been transforming the way organizations function by harnessing valuable information from the largest datasets and providing important insights. With the continued growth of cognitive technologies and increasingly widespread adoption by many industries, what will the future of advanced analytics and AI adoption look like? With the evolution of big data analytics over the past few years, the opportunities to apply this knowledge and to see how different industries are embracing AI and ML has shown tremendous value. However, the evolution and future of analytics doesn't come without challenges. In a recent AI Today podcast interview with Antonio Cotroneo, Director of Technical Content Strategy at OmniSci, spoke about these potential challenges as well as opportunities for industries.
LiveRamp is the leading data connectivity platform. We believe connected data has the power to change the world. Our platform powers insights and experiences centered around the needs of real people, and in ways that keep the Internet open for all. LiveRampers thrive on building together with curiosity and humility--and have a good bit of fun along the way. We're always looking for smart, kind, and creative people to grow our team and impact.
Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the past year or so. Part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the past year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicles, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use trend cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entirely new categories (data observability, reverse ETL, metrics stores, etc.) appearing or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem -- coauthored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019: Part I and Part II, and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym: Machine learning, Artificial intelligence, and Data (MAD) -- this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone -- a macro view that will hopefully be interesting and approachable to most, and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with a deeper familiarity with the industry. Let's start with a high-level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: Why is this happening? How long can it keep going?
Data visualization has become synonymous with business intelligence (BI) data dashboarding. But these dashboards have a weakness: They are only as good as the humans–and AI–that interpret it. For businesses to truly unlock their full operational efficiency potential, they must find a better way to translate data, operationalize metadata, and create more visually intuitive ways to build trust and extract value from the data. One of the reasons behind the lack of trust in the data stems from the absence of context around the numbers to make them useful, especially if the data is needed for a range of purposes, viewed by more than the dashboard creator. And more often than not, when the data isn't our own, we tend to distrust it.
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This blog post has been co-authored by Slawek Kierner, SVP of Enterprise Data & Analytics, Humana and Tie-Yan Liu, Assistant Managing Director, Microsoft Research China. Trips to the hospital happen. And while everyone in the industry strives to deliver world-class care for in-patient experiences, everyone--patients and care teams alike, would prefer to avoid those stays at the hospital. The teams at Humana believed they had enough data to explore the possibility of proactively identifying when patients were heading toward a high-risk event, and they put Microsoft Cloud for Healthcare and AI technology to the test. Humana's questions were straightforward: How do we take the data we have today and use it proactively?
As the digital era evolves, so does marketing. The recent boom in artificial intelligence (AI), machine learning (ML) and big data analytics have led to a new wave of opportunities for those looking to refine their customer engagement strategies. AI is taking over much of the decision-making process for marketers, which has helped eliminate many of the traditional challenges that come with marketing. While AI has been used successfully by marketers for years now, it's only recently that these systems have become powerful enough to take on more complex tasks such as predictive analytics and machine learning. For example, an email service provider can use AI to determine what time a person is most likely to read their emails and send out messages accordingly.
In October 2017, Facebook altered the Instagram API to make it harder for users to search its giant database of photos. The change was a small element of the company's response to the Cambridge Analytica scandal, but it was a significant problem for parts of the Digital marketing industry. Not long before, New York-based influencer marketing agency Amra & Elma had developed a platform that ingested data from Instagram, and allowed its client to use AI image classifiers to find very specific influencers. For instance, they could find an influencer with, say, between 10,000 and 50,000 followers who had posted photos of themselves in a Jeep. Facebook's move killed this capability in a keystroke. Another day in the digital duel between the AIs deployed by digital marketers, and those deployed by the social media platforms.