Accelerating enterprise transformation with AI and Data, for enterprise technology decision-makers-www.mgireservationsandbookings.co.uk
Accelerating enterprise transformation with AI and Data, for enterprise technology decision-makers
www.mgireservationsandbookings.co.uk
The Building Blocks
of an AI Strategy
Organizations need to transition from opportunistic and tactical AI decision-making to more strategic orientation.
As the popularity of artificial
intelligence waxes and wanes,
it feels like we are at a peak.
Hardly a day goes by without an organization announcing “a pivot
toward AI” or an aspiration to “become
AI-driven.” Banks and fintech are using
facial recognition to support know-your customer guidelines; marketing companies
are deploying unsupervised learning to
capture new consumer insights, and retailers are experimenting with AI-fueled
sentiment analysis, natural language processing, and gamification.
A close examination of the activities
undertaken by these organizations reveals
that AI is mainly being used for tactical
rather than strategic purposes — in fact,
finding a cohesive long-term AI strategic
vision is rare. Even in well-funded companies, AI capabilities are mostly siloed or
unevenly distributed.
Organizations need to transition from opportunistic and tactical AI
decision-making to more strategic orientation. We propose an AI strategy built
upon three pillars.
1. AI needs a robust and reliable
technology infrastructure.
Given AI’s
popularity, it is easy to forget that it is not
a self-contained technology. Without a self-contained technology. Without
the support of well-functioning data and
infrastructure, it is useless. Stripped of the
marketing hype, artificial intelligence is little more than an amalgamation of mathematical,
statistical, and computer science techniques that rely on
heavily on a stable infrastructure and usable data.
This infrastructure must include support for
the entire data value chain — from data capture to
cleaning, storage, governance, security, analysis, and
dissemination of results — all in close to real time.
It is not surprising, then, that the AI infrastructure
the market is expected to grow from $14.6 billion in
2019 to $50.6 billion by 2025.
A good infrastructure allows for the establishment of feedback loops, whereby successes and
failures can be quickly flagged, analyzed, and acted
upon. For instance, when Ticketmaster wanted
to tackle the growing problem of opportunists —
people who buy event tickets ahead of genuine
customers, only to resell them at a premium — it
turned to machine learning algorithms. The company created a system that incorporated real-time
ticket sales data along with a holistic view of buyer
activity to reward legitimate customers with a
smoother process and block out resellers. As the
the company soon realized, resellers adapted their
strategies and tools in response to the new system.
Ticketmaster then modified its infrastructure to
include feedback loops, allowing its algorithms to
keep up with the resellers’ evolving techniques.
2. New business models will bring the
largest AI benefits.
AI has the potential to offer
new sources of revenue and profit, either through
massive improvements over the current way of
doing things or by enabling new processes that were
not previously possible. But incremental thinking about how AI can be used will most likely lead to
modest results. Significant benefits are unlikely to
be achieved without a new business model mindset,
or a so-called intelligence transformation.
AI allows for improvements that far surpass
human capabilities. For example, OrangeShark, a
Singapore-based digital marketing startup uses
machine learning for programmatic advertising,
thus automating the process of media selection, ad
placement, click-through monitoring, and conversions, and even minor ad copy changes. Because of
the efficiency offered by its system, OrangeShark is
able to offer a pay-for-performance business model,
whereby clients only pay a percentage of the difference between customer acquisition costs from a
standard advertising model and the OrangeShark model. By completely automating a previously
the semi-automated task, the company has created a
the new business model that makes monetization of
massive efficiency gains possible.
At the other end of the spectrum, Affectiva,
which calls itself an “emotion measurement” company, houses the world’s largest image database of
sentiment-analyzed human faces. The company
analyzes and classifies a range of human emotions
using deep learning models that can then be made
available to clients. Some applications study emotional responses to ad campaigns, while others help
people relearn emotional responses after a stroke.
Affectiva has built a business model based on providing intelligence as a service in an area where
the nonhuman intervention was previously impractical.
These examples merely scratch the surface of
possible AI-enabled business models. We will soon
have smart cameras that facilitate franchising contracts and employee compensation schemes. Machine
learning on granular data will allow for customization of products and services across time. As these and
similar developments open up new sources of revenue
and profit, new business models should therefore be
considered as a foundation of any AI strategy.
3. AI without ethics is a recipe for disaster.
The final AI strategy pillar is ethics, which is not
necessarily a common component of technology
strategy. However, the use of AI raises many
potentially thorny ethical issues, such as incorrect
insights and inherent biases due to poorly constructed
algorithms, and an upswing in unemployment due to
the substitution of human labor with machine output.
Take, for example, facial recognition, one of
the most common AI use cases today. While the
technology has proved to be effective in a number
of areas, such as catching criminals, finding missing people, and even monitoring blood pressure, it
also raises a number of ethical concerns, such as the
right to avoid surveillance and the accuracy of the
algorithms used to identify individuals and groups.
For example, most AI systems are better at accurately identifying people who are white than people
of other ethnicities, and at identifying men’s faces
rather than women’s; indeed, some systems misidentify gender in up to 35% of darker-skinned females.
In December 2018, Google announced that it
would suspend sales of its facial recognition software, citing concerns over ethics and reliability.
5G will help sports venues put the fan in focus
Sports fans are demanding more out of live game experiences. With increasingly robust options for viewing at home and on the go, venue operators need to offer more if they want to compete once the COVID-19 pandemic is under control.
As fans slowly return to live sporting events, it will be more important than ever for stadium owners, leagues, and franchises to collaborate to offer an enhanced experience. Next-generation 5G networks can help them connect to the game in new ways and become more than just passive spectators.
Why 5G?
Fans expect that they’ll be able to connect to their devices smoothly during sporting events, whether it’s to share photos and live experiences of the game, check scores, or text with friends and family. As a result, network traffic in venues grows 50% every year, requiring stadium owners to regularly increase their Wi-Fi and operators’ 4G capacity to keep up. This can be difficult with existing technologies as the capacity upgrades don’t translate into incremental revenues.
Today, everyone can share video live on social media, but 20,000 people doing it at the same time at a concert or game often overloads the network. Sharing experiences with your friends and family are often limited to still photography and text due to overloaded venue capacity.
5G is a better way. In addition to providing expanded capacity that can grow with the network, it brings entirely new experiences for fans, integrating video in new and innovative ways.
Making the in-venue fan experience more interactive
Key stakeholders know they need to offer more to fans in-stadium. The high capacity, low latency, and speed of 5G can bring entirely new experiences. For example, augmented reality capabilities will allow fans to access overlays and commentary, similar to the TV displays and analysis they could see at home, as part of their in-person experience.
Also, increased capacity for video cameras could allow fans in the stands to create a “personal Jumbotron,” allowing them to zoom in on one of the many cameras deployed in the stadium — all from their mobile device.
This could work in the other direction as well, providing a benefit to media organizations. Increased mobile broadband capabilities will allow outlets to send multiple reporters into the stands armed with cellphones, rather than one crew with professional camera equipment. Organizations can also tap into the video from fans, creating dozens or even hundreds of “photographers” to show the excitement within the stands, rather than a professional camera operator zooming in from a distance.
Bringing the live experience home
While the in-stadium experience is paramount, there are ways that 5G can enhance the sports viewing experience away from the arena as well.
Again, here is where better video capabilities can play a role. For example, in motorsports, multiple cameras can be affixed to a car. This isn’t particularly new, but with existing technologies, only one camera can transmit one at a time, and angles are controlled by a single director. With 5G, multiple streams can be supported, and viewers can select their preferred view. For sports games, multiple cameras running simultaneously on a 5G network could allow viewers to pause an exciting goal or touchdown pass and view it from multiple angles.
There are other ways to bring sporting events to fans outside the stadium. For example, Ericsson recently delivered an augmented reality-powered concert. This technology could be used to bring pre-or post-game interviews directly into homes, or even connect with friends at the game.
A fan-focused approach is a good business
5G networks in venues offer business opportunities as well — for stadium owners, as well as app developers. For venue owners, it has been difficult to charge just for Wi-Fi access — it’s like selling salt and pepper in a restaurant when people expect it to be free. But 5G networks offering distinct experiences unavailable over Wi-Fi would be different. People might pay an extra 10% on top of ticket prices. Unlike Wi-Fi, enhanced 5G services could be targeted at certain tiers of ticket holders.
Also, if 5G stadium networks are built by a single network, cell phone users using other service providers’ networks could receive pop-ups on their phone offering access to the 5G network’s extra features for an additional fee.
Once this infrastructure is in place, there will be an app ecosystem focused on enhancing and personalizing the fan experience. This could be from the perspective of the venues (connected to things like parking and concessions) or from the leagues and sports governing bodies (videos, live statistics, etc.)
All this will help venues, team owners, and league officials learn more about their fans in a way you can’t get through paper ticket sales. A lot of this intelligence today is calculated through averages — say there were 30,000 fans and 10,000 beers purchased. 5G will allow drilling down into the data to find trends in concessions, arrival and departure times, where people go after games, etc.
5G and the ability to offer digitally enhanced experiences will be critical as sports venues looking to attract and retain fans for live games — and to stay connected with them away from the stadium. But by keeping the fan in focus, and creating ways to individualize and expand what it means to attend a game, owners can continue innovating on what it means to be a sports fan.
Did you ever wonder what goes on inside the brain of a data scientist?
A few years ago, PerceptiLabs, a deep tech startup, took on an ambitious goal — to visualize what data scientists see when they are building a machine learning model. In doing so, they reinvented the process of model building, making it simpler and faster for experts and beginners alike, to build, train, and analyze their models, so companies could speed up their innovation process.
It’s not news that AI is transforming the world in which we live. Banks are using AI to identify potential fraud, healthcare providers use AI to assist with diagnosis, grocery stores build algorithms to predict consumer behavior, and much more. Today, as businesses rush to accelerate their digital transformations due to COVID-19, AI is becoming more crucial, penetrating more business-critical functions.
To enable AI to do all these great things, the field has generally relied on experts (highly trained data scientists) to build and train complex mathematical models also called machine learning models. This is a complex time-consuming process, involving thousands of lines of code. To see what the models were doing, the experts have to use their imagination to visualize the models in their heads.
As AI and ML took hold and the experience levels of AI practitioners diversified, efforts to democratize ML materialized into a rich set of open-source frameworks like TensorFlow and datasets. Advanced knowledge is still required for many of these offerings, and experts are still relied upon to code end-to-end ML solutions. This can have some advantages when building customized solutions but can require a large investment in resources, infrastructure, and maintenance.
More recently a variety of AutoML tools have launched, promising end-to-end capabilities, where data is input, parameters are adjusted, and a fully-trained, deployable ML model is generated. The simplicity of this sounds inviting — indeed it’s appropriate in certain scenarios — however, ML models created through AutoML often lack transparency into their performance and can be difficult to interpret (i.e., explain why they produce certain results). As well, AutoML solutions often restrict users to only a few ML techniques.
The next generation of ML modeling
PerceptiLabs has developed a next-generation ML tool with our visual modeler that took the best of all worlds: the flexibility of code, some of the automation in connecting components, generating model architectures as well as tuning settings and hyperparameters, combined with the ease of a drag and drop UI.
This makes model-building easier, faster, and accessible to a wider spectrum of users, whether you are an expert or a beginner. There is also the ability to create custom models like simple linear regression, or something more complex like a GAN.
We designed our tool as a visual API on top of TensorFlow, which has grown to become the most popular ML framework. This gives developers full access to the low-level TensorFlow API and the freedom to pull in other Python modules.
Most importantly, users have full transparency into how their model is architected and a view into how their model performs. The result is a new visual approach that’s almost as good as seeing inside a data scientist’s brain!
ML modeling approaches at a glance
There are a lot of choices when it comes to building machine learning models, and each approach needs to be carefully evaluated against the resources you have available to see it through.
That’s why here at PerceptiLabs, we think that our new visual way to build machine learning models, strikes just the right balance across a wide spectrum of ML users while offering better explainability, sophistication, and usability. It’s a flexible but comprehensive approach, that lets you choose the way you want to work, depending on your experience and project needs.
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