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In this weekly series, CNBC takes a look at companies that made the inaugural Disruptor 50 list, 10 years later.

Disruptive companies have shaped the ever-growing consumer packaged goods industry in recent years, from the rise in plant-based products from companies like Beyond Meat and Impossible Foods to an increased focus on personal care products from CNBC Disruptor 50 companies like Beautycounter and Dollar Shave Club.

Consumer behaviors, demands, and expectations have started to flip the industry as well, with shoppers willing to go well beyond a grocery store shelf to find a product they want to buy. The viability of businesses built around direct-to-consumer, e-commerce, and social media has only further accelerated that.

In fact, the top 20 consumer packaged goods companies are estimated to grow five times slower than their smaller category competitors,according to an Accenture report. Add the growth of the category on top of that overall consumer packaged goods volume sales grew 4.3% in 2021 and the emphasis on finding the next big thing has become even more important for companies and investors in the space, as well as the desire for founders with those ideas to access funding.

CircleUp, whose start as a crowdfunding platform that connected accredited investors with food and beverage start-ups landed it on the inaugural CNBC Disruptor 50 list, has looked to evolve alongside the industry. Having already launched its own early-stage investment fund called CircleUp Growth Partners and a credit business that has helped it support more than 500 different brands, its next step is to open its data platform up to the industry to further facilitate more investment.

Danny Mitchell, recently named CircleUp CEO after previously serving as CFO, said that with how quickly the industry is evolving on top of companies like Amazon and Instacart changing how consumers are purchasing products on top of social media platforms, the importance of data in this space is only growing.

"You may have point-of-sale data, or something focused on social media, but you need that holistic view to get a true picture of the category, the trends and the categories, as well as individual companies," Mitchell said. "The Fortune 100 companies in this space are concerned about their existing brands being cannibalized by up-and-coming brands that you may have never even known about or went from 1,000 followers to a million followers on Instagram in six months."

That has also meant staying on top of flavor and ingredient trends with consumers perhaps more willing to try new products than ever before. Mitchell pointed to Asian-inspired sparkling water brand Sanzo, which CircleUp Growth Partners led a $10 million Series A round in February and which features flavors like lychee, calamansi lime, and yuzu ginger.

"You're asking these open-ended questions like is an ingredient as popular today as it was three years ago or even three months?" Mitchell said. "These are the kinds of things that we're trying to constantly analyze and that we can provide clients." Mitchell said Helio, the data platform, should appeal to those Fortune 100 brands trying to stay ahead of the curve with new products while also looking for possible acquisitions, investment firms, and even smaller companies looking for market insights as they grow revenue.

Answering those sorts of questions will likely become even more important as concerns over inflation and a potential recession heighten the focus on consumer spending.

Mitchell said that he believes consumer staples will continue to perform better than peer companies and that many of the early-stage companies that CircleUp is drawing attention to "have product fit but generally have revenue," making some of those bets a bit less risky.

"It's a difficult time but I think that the consumer space will perform better and the opportunities in M&A, and from a bottom-line return from an investment standpoint, are better than the other sectors that we face," he said.

While CircleUp is hoping to facilitate more activity in the CPG space, the company itself does not have any plans to enter the capital markets this coming year, Mitchell said, adding that he expects to the company to "start looking at potential fundraising" next year.

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Why CircleUp thinks machine learning may be the hottest item in consumer goods - CNBC

Of the many ways artificial intelligence and machine learning are poised to improve modern life, the promise of impacting mass transit is significant. The world is much different compared with the early days of the pandemic, and people around the world are again leveraging mobility and transit systems for work, leisure and more.

Across the U.S., traditional mass transit systems including buses, subways and personal vehicles have returned to struggling through gridlock, rider levels and congestion. However, advanced AI and machine learning solutions built on cloud-based platforms are being deployed to reduce these frustrations.

Transportation presents exciting opportunities with AI

Transportation is one of the most important areas in which modern AI provides a significant advantage over conventional algorithms used in traditional transit system technology.

AI promises to streamline traffic flow and reduce congestion for many of todays busiest roadways and thoroughfares. Smart traffic light systems and the cloud technology platforms they operate on are now designed to manage and predict traffic more efficiently, which can save money and create more efficiencies not only for the cities themselves, but for individuals. AI and machine learning today can process highly complex data and traffic trends and suggest optimum routing for drivers in real-time based on specific traffic conditions.

As a result of drastically improved processing power, transit system technologies are now used in various IoT (Internet of Things) devices to achieve real-time image recognition and prediction that took place in legacy data centers during the last half century. This new decentralized-focused architecture helps increase the implementation of machine learning and AI.

Todays recognition algorithms offer enhanced insight on the mix of density, traffic and overall rate of flow. Furthermore, these optimized algorithms can leverage data points by region resulting in a streamline pattern to reduce traffic problems while redistributing flow more optimally. Municipal transit systems can then make better decision-making power, and the control system has a much higher degree of failure tolerance as was previously demonstrated in legacy hub-and-spoke systems.

AI is already impacting transit systems

These technologies are already being deployed around the country. As one example, the Santa Clara Valley Transportation Authority in partnership with the City of San Jos, California, has been piloting a cloud-based, AI-powered transit signal priority (TSP) system that utilizes pre-existing bus-fleet tracking sensors and city communication networks to dynamically adjust the phase and timing of traffic signals to provide sufficient green clearance time to buses while minimally impacting cross traffic.

Because the new platform leverages pre-existing infrastructure, it required no additional hardware installations inside traffic signal cabinets or buses. And unlike traditional, location-based check-in and check-out TSP solutions, the platform processes live bus location information through machine learning models and makes priority calls based on estimated times of arrival. The platform has so far improved travel times on VTAs route 77 by 18% to 20% overall, equating to a five- to six-minute reduction in signal delay.

The cloud-based transit signal priority system combines asset management and automation to produce a system capable of providing services to an entire region. Unlike hardware-based systems, this platform uses pre-existing equipment and leverages cloud technology to facilitate operations. This removes the need for vehicle detection hardware at the intersection because vehicle location is known through the CAD/AVL system. This enables both priority calls from greater distances away from signals and priority calls coordinated among a group of signals. Furthermore, the system provides real-time insights on which buses are currently receiving priority along with daily reports of performance metrics.

The advanced transit signal priority systems available today consist of two parts, a unit in the traffic cabinet and another unit placed on the vehicle. The transit priority logic is the same, regardless of the detection and communication medium. When a vehicle is within predetermined boundaries, the system places a request to the signal controller for prioritization. Since the original systems used fixed detection points, signal controllers were configured with static estimated travel times. Since travel times are dependent on several environmental factors, the industry implemented GPS based, wireless communication systems. With this method, vehicles found within detection zones replace the static detection points and the vehicles speed is used to determine arrival time.

The platform allows cities to build upon current investments in infrastructure to deploy city-wide TSP. To enable safe and secure connections with traffic signals, each city requires just one device for use that is a computer that resides at the edge and serves as the protective link between city traffic signals and the platform. It is designed to securely manage the information exchange between traffic lights and the cloud platform. It is the only additional hardware necessary, and depending on the existing city network configuration, the platform may receive vehicular data directly or via the citys network using secure connections.

Sophisticated process for prioritizing traffic

The systems method of placing priority calls to traffic signals is more sophisticated and is not constrained to fixed-point locations. Unlike the current state-of-the-art of placing priority calls from the detection of buses at specific locations that starts a pre-programmed time of arrival, this platform uses a vectorized approach. In mathematics, a vector is an arrow representing a magnitude and a direction. In this platforms software, the arrow points in the direction of the traffic light and the magnitude is the travel time.

When the system is set up, traffic signals, bus routes and bus stops all get a digital representation on this vector. This ends up producing a digital geospatial map where software is then able to track bus progression along bus routes. This results in a system that can dynamically place transit calls regardless of its location. Instead, the system makes precise priority calls based on the expected time of arrival which is the basis for all TSP check-in calls supported by all signal controller vendors. And due to the nature of the tracking algorithm, any significant changes to ETA can be adjusted. For example, if a bus was predicted to skip a bus stop but didnt, the system will detect the change and adjust the priority call accordingly.

The combination of AI, machine learning and cloud-based technology all have great potential to not only improve the current mass transit system but reimagine it all together. This advanced technology is already proving how it can improve coordination between GPS, navigational apps, connected autos, and even taxi and ride-sharing services to efficiently combine into a single transit entity based on real-time data.

In the not-too-distant future, it is expected that connected self-driving cars and trucks will be more prevalent on the roads and highways, offering even greater potential for AI to reduce both the duration and risk of rapid mobility.

Timothy Menard is the Founder and chief executive officer of LYT, provider of cloud-based smart traffic solutions. LYT makes traffic lights smart by enabling them to see and respond to traffic. By doing so LYT can prioritize first responders and public transportation vehicles so they can get to their destinations faster and safer. The additional benefit is that it streamlines overall traffic flow helping to reduce congestion and emissions in high traffic areas.

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How AI and machine learning are reshaping the way transit systems move traffic patterns REJournals - REjournals.com

SAN FRANCISCO, May 27, 2022 (GLOBE NEWSWIRE) -- Syapse, a leading real-world evidence company dedicated to extinguishing the fear and burden of serious diseases by advancing real-world care, today announced two new studies focused on how the use of machine learning on real-world data can be used to power precision medicine solutions. Syapse will be presenting at the American Society for Clinical Oncology (ASCO) Annual Meeting being held June 3-7, 2022 in Chicago.

This years ASCO is centered on a theme of innovation to make cancer care more equitable, convenient and efficient. Two studies that we are presenting align well with this objective, with a focus on how machine learning can be applied to real-world data to better bring identification of patient characteristics, and specific patient cohorts of interest, to scale, said Thomas Brown, MD, chief medical officer of Syapse. The transformational effort to pursue more personalized, targeted treatments for patients with cancer can be empowered by leveraging real-world data to produce insights in the form of real world evidence, as a complement to classical clinical trials.

Unveiled at ASCO, the Syapse studies include:

In addition to presenting this research at ASCO, Syapse has created an online ASCO hub with more information about its research, its interactive booth experience and how its work with real-world evidence is transforming data into answers that improve care for patients everywhere. For ASCO attendees, please visit Syapse at booth #18143 during the show.

AboutSyapseSyapse is a company dedicated to extinguishing the fear and burden of oncology and other serious diseases by advancing real-world care. By marrying clinical expertise with smart technologies, we transform data into evidenceand then into experiencein collaboration with our network of partners, who are committed to improving patients lives through community health systems. Together, we connect comprehensive patient insights to our network, to empower our partners in driving real impact and improving access to high-quality care.

Syapse ContactChristian Edgington, Media & Engagementcedgington@realchemistry.com

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Syapse Unveils Two New Studies on Use of Machine Learning on Real-World Data to Identify and Treat Cancer With Precision at ASCO 2022 - GlobeNewswire

Artificial intelligence is todays most discussed and debated technology, generating widespread adulation and anxiety, and significant government and business interest and investments. But six years after DeepMind's AlphaGo defeated a Go champion, countless research papers showing AIs superior performance over humans in a variety of tasks, and numerous surveys reporting rapid adoption, what is the actual business impact of AI?

Human intelligence communicating with the artificial kind. (Photo by Jonas Gratzer/LightRocket via ... [+] Getty Images)

2021 was the year that AI went from an emerging technology to a mature technology... that has real-world impact, both positive and negative, declared the 2022 AI Index Report. The 5th installment of the index measures the growing impact of AI in a number of ways, including private investment in AI, the number of AI patents filed, and the number of bills related to AI that were passed into law in legislatures of 25 countries around the world.

There is nothing in the report, however, about real-world impact as I would define itmeasurably successful, long-lasting and significant deployments of AI. There is also no definition of AI in the report.

Going back to the first installment of the AI Index report, published in 2017, still does not yield a definition of what the report is all about. But the goal of the report is stated upfront: the field of AI is still evolving rapidly and even experts have a hard time understanding and tracking progress across the field. Without the relevant data for reasoning about the state of AI technology, we are essentially flying blind in our conversations and decision-making related to AI.

Flying blind is a good description, in my opinion, of gathering data about something you dont define.

The 2017 report was created and launched as a project of the One Hundred Year Study on AI at Stanford University (AI100), released in 2016. That studys first section did ask the question what is artificial intelligence? only to provide the traditional circular definition that AI is what makes machines intelligent, and that intelligence is the quality that enables an entity to function appropriately and with foresight in its environment.

So the very first computers (popularly called Giant Brains) were intelligent because they could calculate, even faster than humans? The One Hundred Year Study answers Although our broad interpretation places the calculator within the intelligence spectrumthe frontier of AI has moved far ahead and functions of the calculator are only one among the millions that today's smartphones can perform. In other words, anything a computer did in the past or does today is AI.

The study also offers an operational definition: AI can also be defined by what AI researchers do. Which is probably the reason this years AI Index measures the real-world impact and progress of AI, among other indicators, by the number of citations and AI papers (defined as AI by the papers authors and indexed with the keyword AI by the publications).

Moving beyond circular definitions, however, the study provides us with a clear and concise description of what prompted the sudden frenzy and fear around a term that was coined back in 1955: Several factors have fueled the AI revolution. Foremost among them is the maturing of machine learning, supported in part by cloud computing resources and wide-spread, web-based data gathering. Machine learning has been propelled dramatically forward by deep learning, a form of adaptive artificial neural networks trained using a method called backpropagation.

Indeed, machine learning (a term coined in 1959) or teaching a computer to classify data (spam or not spam) and/or make a prediction (if you liked book X, you would love book y), is what todays AI is all about. Specifically, since its image classification breakthrough in 2012, its most recent variety or deep learning, involving data classification of very large amounts of data with numerous characteristics.

AI is learning from data. The AI of the 1955 variety, which generated a number of boom-and-bust cycles, was based on the assumption that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. That was the vision and, by and large, so far it hasnt materialized in a meaningful and sustained way, demonstrating significant real-world impact.

One serious problem with that vision was that it predicted the arrival in the not-so-distance future of a machine with human intelligence capabilities (or even surpassing humans), a prediction reiterated periodically by very intelligent humans, from Turing to Minsky to Hawking. This desire to play God, associated with the old-fashioned AI, has confounded and confused the discussion (and business and government actions) of present-day AI. This is what happens when you dont define what you are talking about (or define AI as what AI researchers do).

The combination of new methods of data analysis (backpropagation), the use of specialized hardware (GPUs) best suited for the type of calculations performed, and, most important, the availability of lots of data (already tagged and classified data used for teaching the computer the correct classification), is what led to todays AI revolution.

Call it the triumph of statistical analysis. This revolution is actually a 60-year evolution of the use of increasingly sophisticated statistical analysis to assist in a wide variety of business (or medical or governmental, etc.) decisions, actions, and transactions. It has been called data mining and predictive analytics and most recently, data science.

Last year, a survey of 30,000 American manufacturing establishments found that productivity is significantly higher among plants that use predictive analytics. (Incidentally, Erik Brynjolfsson, the lead author on that study has also been a steering committee member of the AI Index Report since its inception). It seems that its possible to find a measurable real-world Impact of AI, as long as you define it correctly.

AI is learning from data. And successful, measurable, business use of learning from data is what I would call Practical AI.

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What Is AI? Understanding The Real-World Impact Of Artificial Intelligence - Forbes

Artificial intelligence (AI) and machine learning (ML) have taken the world by storm. From music to loan and credit card recommendations, solutions powered by AI and ML are fast penetrating into many aspects of lives.

Today every enterprise wishes to add AI and ML to their technology mix. In an Accenture survey, 84% of executives said they wouldnt achieve their growth objectives without scaling AI. AI and ML are gaining high traction as enterprises can gain competitive differentiation and accelerate their business growth using solutions powered by these technologies.

AI and ML-based solutions augment human effort in ways that help enterprises optimize their costs, enhance operational efficiency, and deliver customer-centric services. In a McKinsey survey, most survey respondents said their organizations had adopted AI capabilities, as AIs impact on both the bottom line and cost saved is growing.

As the impact of AI and ML grows manifold, lets learn about the top advantages enterprises can gain by implementing these technologies:

Enterprises are always looking for ways to enhance customer engagement to improve acquisition and retention. AI and ML empower enterprises to understand their customers at a deeper, personal level by generating insights based on customer behaviour and their transaction history. These insights help enterprises enable personalized customer engagement and deliver tailor-made offerings. Enterprises can also explore cross-sell and up-sell opportunities by anticipating customers needs. For instance, banks can provide customized loan recommendations and financial offerings based on customers credit history and risk appetite.

Today customers expect round-the-clock assistance while using services on their preferred channels. To improve customer support services and communications, enterprises can deploy an AI-enabled virtual assistant or chatbot system that helps customers find solutions to their queries on demand. For example, insurance companies can quickly cater to customer queries by deploying self-service portals. Customer support teams can also leverage insights generated through AI systems to gain context into the customer journey and provide better assistance.

Investing in technologies like AI and ML significantly enhances the accuracy of complex business processes. AI and ML systems have self-learning capabilities. These systems become more intelligent as more data is fed. Enterprises can leverage these systems dynamic, trainable capabilities to enable accurate content extraction, automatic document classification, and efficient sentiment analysis. This also ensures effective content governance and streamlined content-centric processes. For instance, banks can accelerate customer onboarding processes with the help of document verification and various identity verification tools powered by AI.

As data volumes and competition are growing simultaneously, enterprises must stay ahead of the curve and futureproof themselves. Predictive analytics, enabled through AI and ML, can help enterprises minimize risks, ensure intelligent decision-making, and improve overall business outcomes. Banks can utilize predictive analytics for assessing loan applications based on customers previous transactions. Similarly, insurance companies can leverage analytics for risk prevention and fraud detection. Also, retailers can ensure intelligent decision-making through AI-enabled predictive analytics to prevent instances of understocking and overstocking.

To Conclude

AI and ML can tremendously contribute to an organizations growth irrespective of its size or sector. However, business leaders must refrain from outrightly implementing these technologies without the right context. To successfully leverage AI and ML, leaders must identify the areas where these technologies can add value and implement them based on their suitability.

As more data is added to global servers, scaling AI and ML technologies will be critical for enterprises to transform their data into data wealth in the true sense.

Views expressed above are the author's own.

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How ArtificiaI Intelligence and Machine Learning are transforming the business landscape - Times of India