Integrating Disruptel’s capabilities within Samba TV and the existing Samba AI technology infrastructure will help enable the next generation of cutting-edge content recognition technology and non-intrusive ad unit development. The acquisition will also help drive expanded capabilities for both television manufacturers and publishers, while creating a more immersive and engaging viewing experience for consumers with on-screen content information and recommendations.

“Artificial intelligence and machine learning are transforming nearly every aspect of our lives today, and we are excited about its growing relevance in television,” said Samba TV Co-Founder and CEO Ashwin Navin. “After unveiling Samba AI at CES, we have been making critical investments in Samba AI to support every facet of the Connected TV ecosystem. From the same company that introduced content recognition algorithms in Connected TVs, we are excited to share how AI will unlock a more engaging viewing experience for the content we love. Samba TV continues to lead the market in providing the technical innovations shaping the future of television with big data and machine learning.”

Disruptel’s machine learning technology intelligently deconstructs the on-screen content in real-time, identifying the people, brands and products that appear. This ML-powered video analysis goes beyond just the program or ad appearing on the TV, and can understand product placement, logo appearances, and integrated advertising which will provide advertisers deeper insight into the value of their investments across every content platform viewed on the biggest screen in the home.

“AI is the future of TV, and Samba TV shares our vision for what that will look like,” said Disruptel CEO Alex Quinn. “With more TV data, in more countries, and from more TV brands than any other company in the world, Samba AI is the perfect home for our machine learning expertise so that we can be in a position to lead the industry in AI.”

Samba TV will integrate Disruptel’s technology into Samba AI, enabling a more immersive viewing experience for linear and streaming audiences. Samba AI is a turnkey solution that allows television manufacturers to offer a “smart pause” functionality that displays supplemental information about on-screen actors and actresses, as well as non-intrusive pause ad units that are embedded within the additional information. Samba TV plans to unveil new Samba AI-driven experiences for TV manufacturers in January at CES. Later in 2023, the company expects to deploy Samba AI technology to support advanced content recommendations, in-stream brand experiences, and new innovative interactive features.

Navin continued, “This acquisition brings us closer to the living room viewing experience we have all been dreaming about, where consumers are empowered to go deeper into the content they are viewing, advertisers will gain greater insights into the value of their sponsorships and TV manufacturers will capture new opportunities to differentiate their incredible products.”

To learn more about Samba TV, visit the website here.

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This latest financing round was led by Sierra Ventures, with participation from the company’s pre-seed lead investor Origin Ventures and brings its total financing to date to $27 million. Hammoq startup has Automated More than a Half Million Resale Product Listings Across a Broad Network of eCommerce Marketplaces Using Artificial Intelligence and Machine Learning

A glut of online retail returns and a flourishing market for sustainable fashion upcycling and vintage items has created an exploding reCommerce industry. With this financing round, Hammoq will accelerate development of its AI and machine learning platform to automate marketplace listings and open up channels for reseller financing as well as product sourcing.

“The reCommerce market is exploding, with the fashion resale market alone expected to reach $26 billion in 2022,” said Sid Lunawat, CEO and Co-Founder of Hammoq. “There are an abundance of resale marketplaces supporting this economy. Yet, the largest gap is in the labor required to identify product value and push it to the marketplaces. With rising labor costs, the need for automation in the reCommerce industry continues to grow. Using AI and machine learning, Hammoq has put a major dent in this labor gap, empowering its customers to automate the identification and listing process. Our latest funding will accelerate tech development while supporting our sales and marketing strategies to capitalize on this rising opportunity.”

Goodwill of the San Francisco Bay and Hammoq share a deep commitment to sustainability. Hammoq’s AI technology is helping Goodwill to process more material donations, more quickly to help divert more items from landfill. Donated items are given a second life in Goodwill thrift stores.

Another customer of Hammoq is reseller Flip the World. “Before we brought Hammoq on to handle our listings, I would average around 40-50 listings a week. Listing across platforms is time consuming work and after a full day of sourcing, cleaning, prepping and photographing I found that I was falling asleep in bed trying to get my listings up,” said Chris Hatfield, Owner, Flip the World. “Now with Hammoq, we’ve more than doubled our weekly listings and have gone from a 500-item store to over 2,000 in less than two months. It’s been a game changer for our business. Now we can focus on the most important part of reselling, sourcing the items, rather than the listing.”

Hammoq was co-founded by reCommerce industry veterans Sid Lunawat and Ty Blunt, sustainability advocates who believe the 80 billion resalable items dropped in landfills every year have a viable purpose for prolonged use. After successful stints running a reseller business, Lunawat and Blunt understood that identifying and easily listing products for resale was an acute pain point. To solve this challenge, they developed the Hammoq software platform using AI and machine learning to enable listing at scale. Starting with a simple photo, the platform uses product data to automatically enable listings across dozens of resale marketplaces. Hammoq has listed over 600,000 products for sale and ensures 100% compliance with data listing requirements prior to pushing to the marketplaces.

“Hammoq is uniquely serving the exploding reCommerce industry by applying technology to solve sellers’ largest challenge: intelligent, real-time listings,” said Vignesh Ravikumar, Partner, Sierra Ventures. “Their solution and business model are well aligned with our mission to invest early in emerging technology companies that are moving the needle in key market areas. Their SaaS reCommerce solution is delivering precisely what the rapidly transforming market needs as consumers actively seek ways to buy used products that support sustainability and reuse.”

Hammoq is a company that is reimagining reCommerce, digitizing resale goods and automating online listings so customers can list and sell more. Established in 2021 and based in Phoenix, Hammoq has supported the identification and listing automation process for more than 600,000 items. For resellers, liquidators, thrift and retail organizations, Hammoq enables the resale of returns, lost SKUs, overstocks and other pre-loved goods. Built by resellers for resellers Hammoq’s SaaS platform drives customers’ ability to scale reCommerce, while significantly reducing the time and labor needed to get merchandise online.

Sierra Ventures is a Silicon Valley-based early-stage venture firm investing globally with a focus on Core Enterprise and Next-Gen Technologies. With four decades of experience and over $2 billion of assets under management, Sierra has created a vast network of successful entrepreneurs, Global 1000 CXOs, operational executives, and deep domain experts, providing a platform for entrepreneurs around the world.

Learn more about Hammoq at the website here.

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Real-time deep learning systems are very powerful and form the core AI models of social media platforms such as Facebook, search engines such as Google, video platforms such as YouTube and mobile apps such as Uber. These systems enable virtuous feedback loops that increase customer engagement and retention. For example, on YouTube, these models recommend live sports videos when a user has just watched an Olympics video and late night comedy clips when their intent changes and they are in the mood to laugh.

With Abacus.AI, all organizations, not just big tech, can build large-scale, real-time enterprise AI systems with ease. Data science teams in these organizations can either use Abacus.AI’s neural architecture search to train models, or specify their models on popular frameworks such as TensorFlow and Pytorch, and let Abacus’s end-to-end AI platform take care of the rest.

Abacus.AI features all the key components of an end-to-end AI service, including easy set up of data pipelines, data cleaning and transformation, model training and hosting, a real-time ML feature store service, model monitoring, and explainability.  Abacus.AI follows the principle of keeping simple things simple, while making complex things possible. The service allows ML beginners to build and host simple ML models through an intuitive UI, while advanced ML practitioners and experienced data scientists can leverage APIs, and write python code and SQL queries to build their own models.

The real-time deep-learning system can be used for both supervised and unsupervised learning and can be applied across a variety of use-cases including newsfeed personalization, personalized search, natural language processing, e-commerce recommendations, predictive maintenance in factories, cloud spend monitoring, and alerting systems that detect unexpected events.

While data science teams have the option to bring their own models, customers can also choose to use Abacus’s expert AI engine to develop custom deep-learning models that are based on the use-case and dataset.

These deep-learning models choose the best neural architecture from an array of different types of NNs including LSTMs, RNNs, Transformers and Variational Auto-encoders. In order to train these custom models, Abacus.AI has invented several new neural architecture techniques.

These techniques have been published as research papers at top AI/ML conferences including NeurIPs and ICML. A list of recent publications can be found here – https://abacus.ai/publications. Recently, the company applied its techniques and participated in the prestigious CVPR competition, Unseen Data in Neural Architecture Search, and placed 2nd.

Once trained, these models can easily be evaluated using a comprehensive model metrics dashboard, pushed to production and continuously re-trained on a regular schedule or whenever significant feature drift is detected. Abacus.AI provides dashboards to monitor latency, traffic, errors, and feature drift of models in production. This removes all the heavy lifting needed by data science teams to operationalize and run models.

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In the new study, Thriving in an AI World, high numbers of business leaders from the following industries say AI is at least moderately functional in their organizations, including those in:  industrial manufacturing (93 percent), financial services (84 percent), tech (83 percent), retail (81 percent); life sciences (77 percent), healthcare (67 percent) and government (61 percent).  In addition, several industries saw a significant increase from last year’s report: financial services (37-percentage point increase), retail sector (29-percentage point increase) and tech sector (20-percentage point increase).

According to Traci Gusher, Principal of Artificial Intelligence, “Leaders are experiencing COVID-19 whiplash, with AI adoption skyrocketing as a result of the pandemic. But many say it’s moving too fast. That’s probably because of current debate surrounding the ethics, governance and regulation of AI.  Many business leaders do not have a view into what their organizations are doing to control and govern AI and may fear risks are developing.”

Specifically, half of business leaders in industrial manufacturing (55 percent), retail and tech (49 percent in each) say AI is moving faster than it should in their industry. Concerns about the speed of AI adoption are particularly pronounced among small companies (63 percent), business leaders with high AI knowledge (51 percent), and Gen Z and Millennial business leaders (51 percent).

Leaders confident in AI solving industry problems, including COVID-19
Business leaders from both small (88 percent) and large (80 percent) companies say AI technology helped their company during the COVID-19 outbreak. As we continue to navigate the pandemic, life sciences and healthcare business leaders are overwhelmingly confident in AI’s ability to monitor the spread of COVID-19 cases (94 percent and 91 percent), help with vaccine development (90 percent and 94 percent) and distribution (90 percent and 88 percent), respectively.

Beyond the pandemic, business leaders are confident in AI’s ability to solve major industry problems. Specifically, these include:

• Financial services business leaders surveyed are confident in AI’s ability to detect fraud (93 percent), higher than last year’s report (85 percent).
• And government decision-makers surveyed are confident in AI’s ability to improve bureaucratic efficiency (79 percent).

Business leaders optimistic about new administration, want more regulation
Business leaders across industries believe the Biden administration will do more to help advance the adoption of AI in the enterprise: industrial manufacturing (90 percent), tech (88 percent), retail (85 percent), financial services (82 percent), life sciences (81 percent), government (79 percent) and healthcare (73 percent).

Additionally, younger (Gen Z and Millennial) business leaders are more likely (90 percent) than older (Gen X and Baby Boomer) business leaders (79 percent) to be more optimistic about the potential actions the Biden administration will do to help advance the adoption of AI in enterprise.

Even with the optimism, business leaders are conscious that controls are needed and overwhelmingly believe the government has a role to play in regulating AI technology: industrial manufacturing (94 percent), retail (87 percent), financial services (86 percent), life sciences (86 percent), tech (86 percent), healthcare (84 percent) and government (82 percent). Business leaders with high AI knowledge (92 percent) are more likely to say the government should be involved in regulating AI technology in comparison to total business leaders (87 percent).

Compared to last year’s report, business leaders are more interested in government involvement, with financial services increasing by 27-percentage points, retail increasing by 24-percentage points, and tech increasing by 17-percentage points.

“We are seeing very high levels of support this year across all industries for more AI regulation.  One reason for this may be that, as the technology advances very quickly, insiders want to avoid AI becoming the ‘Wild Wild West.’ Additionally, a more robust regulatory environment may help facilitate commerce. It can help remove unintended barriers that may be the result of other laws or regulations, or due to lack of maturity of legal and technical standards,” said Rob Dwyer, Principal, Advisory, specializing in technology in Government.

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“Advancement leaders in 2020 acknowledged the need reassess our true North. The tried and true methods we used in the past to secure philanthropic support for our nonprofit organizations must adapt and innovate,” said Reed Sheard, Ed.D, CIO and Vice President of Advancement for Westmont College and Chair of the AAAC. “Finding a new model for successful fundraising and artificial intelligence presents an incredible opportunity to move significant donor relationships forward at scale. I’m incredibly proud to present this year’s State of AI in Advancement report along with my fellow AAAC members because it lays the foundation for transformative change in our industry.”

During the COVID-19 crisis, the landscape of advancement changed dramatically. From staff and budget cuts to developing a new understanding of donor relations in a purely digital format, to reimagininig how to significantly grow new pipeline in order to offset revenue disruption — in many ways advancement professionals are increasingly being pushed to transform how fundraising operates. The 2020 AAAC State of AI in Advancement report specifically examines these industry shifts and finds that 82 percent of advancement professionals believe AI is a part of the long-term solution.

“This year, more than any other, advancement as a whole has been forced to face the uncomfortable and adapt to ensure future growth. The intersection with artificial intelligence is inevitable,” said Adam Martel, CEO of Gravyty and AAAC member. “This year’s State of AI in Advancement not only proves the leaders are already adopting AI to solve their largest challenges, it also is an amazing guide that shows three exciting areas where AI is yielding tangible results.”

The AAAC’s top-line findings from the 2020 State of AI in Advancement include:

AI adoption is becoming a priority

• 82% believe that AI will be part of the solution for challenges facing fundraising teams as a result of COVID-19
• 66% of nonprofit organizations are considering investment in new tools and technologies to address challenges as a result of COVID-19

There’s a new and accelerated need for workforce efficiency within advancement

• 73% of nonprofit organizations faced budget reductions and/or hiring freezes in 2020

New roles within advancement are emerging

• 62% believe that some personnel will be shifted to the role of a digital gift officer

As need for pipeline development grows, so does belief in AI

• 82% believe that AI can effectively prioritize and reach more prospects

The full AAAC 2020 State of AI in Advancement report is available here. The 2020 State of AI in Advancement report was compiled from the AAAC’s volunteer members. The data used for all sections of this report comes from surveys of advancement professionals conducted between March 3, 2020, and July 29, 2020. In total, 337 advancement professionals participated in these surveys.

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The Private AI Institute was originally established by Intel’s University Research & Collaboration Office (URC), which then expanded the collaborative potential of the Institute by inviting Avast and Borsetta to join forces. Together, the parties issued a call for research proposals earlier this year, and selected the first nine research projects to be supported by the Private AI Collaborative Research Institute at eight universities:

• Carnegie Mellon University, U.S.
• University of California, San Diego, U.S.
• University of Southern California, U.S.
• University of Toronto, Canada
• University of Waterloo, Canada
• Technical University of Darmstadt, Germany
• Université Catholique de Louvain, Belgium
• National University of Singapore

The Private AI Collaborative Research Institute will encourage and support fundamental research which will result in solving such real-world challenges for society, and will be dedicated to taking an ethical approach to AI development. By decentralizing AI, and moving AI analytics to the network edge, the companies aim to liberate data from silos, protect privacy and security, and maintain efficiency.

The Private AI Collaborative Research Institute will aim to overcome a number of challenges faced by industries and societies today. This includes but is not limited to:

• Training data is decentralized in isolated silos and often inaccessible for centralized training.
• Today’s solutions requiring a single trusted data center are brittle – centralized training can be easily attacked by modifying data anywhere between collection and the cloud. There is no existing framework for decentralized secure training among potentially untrusting participants.
• Centralized models become obsolete quickly as data at the edge changes frequently. Today, infrequent batch cycles for collection, training and deployment can lead to outdated models; continuous and differential retraining is not possible.
• Centralized computing resources are costly and throttled by communication and latency.
• Federated Machine Learning, a technique used to train an algorithm across multiple decentralized edge devices, is limited. While today’s federated AI can access data at the edge, it cannot simultaneously guarantee accuracy, privacy, and security.

Michal Pechoucek, Chief Technology Officer at Avast added, “With our skilled AI research team, and Avast AI and Cybersecurity Laboratory (AAICL) located on campus at The Czech Technical University (CTU), we are already witnessing the great results from our scientific research into the intersection of AI, ML, and cybersecurity. Industry and academic collaboration is key to tackle the big issues of our time, including ethical and responsible AI. As AI continues to grow in strength and scope, we have reached a point where action is necessary, not just talk. We’re delighted to be joining forces with Intel and Borsetta, to unlock AI’s full potential for keeping individuals and their data secure.”

Borsetta has joined the Private AI Collaborative Research Institute because of their strong belief in driving a privacy-preserving framework to support our future hyperconnected world empowered by AI. Pamela Norton, CEO of Borsetta, stated, “the mission of the Private AI Collaborative Institute is aligned with our vision for future proof security where data is provably protected with edge computing services that can be trusted. Trust will be the currency of the future, and we need to design AI embedded edge systems with trust, transparency, and security while advancing the human-driven values they were intended to reflect.”

Richard Uhlig, Intel Senior Fellow, Vice President and Director of Intel Labs, said: “AI will continue to have a transformational impact on many industries, and is poised to become a life-changing force in healthcare, automotive, cybersecurity, financial and technology industries. That said, research into responsible, secure, and private AI is crucial for its true potential to be realized. The Private AI Collaborative Research Institute will be committed to the advancing technologies using ethical principles that put people first and keep individuals safe and secure. We invited Avast and Borsetta to join us on our mission to identify the true impact of AI on the world around us. We are excited to have them on board to mitigate potential downsides and dangers of AI”

For more details about the center, and to follow the exciting research discoveries in future, please visit: https://www.intel.com/content/www/us/en/research/blogs/private-ai-collaborative-research-institute-launch.html

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“The commitment of AMD to technological innovation aligns with our mission to develop and accelerate the adoption of the hybrid cloud to help connect, secure and power our digital world,” said Dario Gil, Director of IBM Research. “IBM is focused on giving our clients choice, agility and security in our hybrid cloud offerings through advanced research, development and scaling of new technologies.”

“This agreement between AMD and IBM aligns well with our long-standing commitment to collaborating with leaders in the industry,” said Mark Papermaster, executive vice president and CTO, AMD. “AMD is excited to extend our work with IBM on AI, accelerating data center workloads, and improving security across the cloud.”

For many companies, securing highly sensitive data still remains a challenge: cybersecurity is currently the top barrier for adoption as well as the top criteria for selection of cloud providers, according to data from IBM’s Institute for Business value.

According to Gartner, Confidential Computing potentially removes the remaining barrier to hybrid cloud adoption for highly regulated businesses or any organization concerned about unauthorized third-party access to data in use in the public cloud.

Confidential Computing is a technology, enabled by hardware, that allows the data associated with a running virtual machine (VM) to be encrypted, including while workloads are running. This capability helps prevent would-be attackers and bad actors from accessing confidential information, even in the event of a break-in. Confidential Computing for hybrid cloud unlocks new potential for enterprise adoption of hybrid cloud computing, especially in regulated industries such as finance, healthcare and insurance.

Engagement between AMD and IBM researchers on joint development activities under the agreement is now underway.

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The “Scaling up AI across the life science value chain: Enhancing R&D, creating efficiencies and increasing impact” report summarizes key findings from that survey, and offers recommendations for how life sciences companies can gain immediate returns on investment and experience a competitive advantage over the longer term.

From R&D to manufacturing, supply chain to commercial functions, AI is beginning to have an impact on increasing efficiencies across the biopharma value chain, especially as a result of the COVID-19 pandemic. In addition, increased remote work environments helped life sciences leaders realize how effective digital solutions can be in helping their businesses run smoothly, transforming mindsets and enabling executives to lean into a future grounded in digitization, data and AI. While today AI is primarily applied to automate existing processes, additional potential lies in accelerating insights and discovery to expedite access to new therapies and products. AI’s potential stretches across the value chain from identifying and validating genetic targets for drug development, designing novel compounds, expediting drug development, making supply chains smarter and more responsive, to helping launch and market products. Life sciences companies that deploy AI strategically across the entire enterprise will likely be best positioned to maximize their AI investments and achieve a competitive edge.

“The life sciences industry has only begun to scratch the surface of AI’s potential but the good news is biopharma and life sciences leaders see the potential and are willing to make the investments necessary to realize what’s possible. They should be cautious, though, and carefully plan and strategize so those investments are used wisely and result in the desired outcomes. By spending time on a solid strategy, putting the building blocks in place for success and leveraging relationships with relevant partners, AI can help transform the life sciences industry as we know it and get the necessary products to market more quickly,” said Aditya Kudumala, principal, Deloitte Consulting LLP.

Investing in AI
Biopharma and medtech leaders surveyed agree that life sciences organizations are investing in AI, but “how” they’re investing varies widely. When asked about their company’s investments in AI projects and technologies in the past year:

• Forty-one percent invested $20-50 million; followed by 26% that invested $10-20 million; and 21% that invested more than $50 million in AI initiatives.
• More than half of survey respondents expect investments in AI will continue to increase throughout 2020 as AI moves from tactical improvements in efficiency to larger-scale, organization-wide projects.
• Forty-six percent of respondents said it takes longer than expected to receive payback from investments in AI initiatives.

Enhanced efficiency
When it comes to the top outcomes life sciences companies hope to achieve by using AI, three primary objectives emerged:

1. Enhance existing products (28%)
2. Create new products and services (27%)
3. Make processes more efficient (22%)

The top three outcomes where respondents said they’ve seen the most success with AI include:

1. Making processes more efficient (43%)
2. Improving decision making (41%)
3. Enhancing existing products and enabling new business models (both 40%)

Understanding risks
As with any new technology, survey respondents pointed to multiple challenges associated with AI implementation, including poor-quality data, siloed data systems, integrating AI into legacy systems and risk management. The top challenges life sciences companies cited when implementing AI were:

• Identifying use cases with the greatest business value (30%)
• Integrating AI into the organization (28%)
• Data challenges (28%)
• Challenges implementing AI technologies (28%)

Moving AI forward 
In order to take AI to the next level, Deloitte recommends life sciences leaders consider the following three step process:

• First, company leaders should define their vision and strategy for how they want to take advantage of AI and what they seek to achieve from their investments in data, analytics and AI.
• To carry out that strategy, companies should then assemble the building blocks to help set themselves up towards success. The core building blocks Deloitte advises leaders to consider include: technology and data, technology architecture, ecosystems and alliances, talent and aligning on risk management.
• Third, leaders should evaluate ways to scale up internal investments or partnerships that can help their companies go from short-term to longer-term success in the market.

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“Kryon welcomes our latest channel partners to the fold. Kryon’s strategy is to help its partners stand out in the market and create superior impact on customers. We look forward to working closely with these organizations from all over the world that share our belief that RPA is an explosive market poised for widespread adoption over the next three years,” said Harel Tayeb, CEO of Kryon. “Together, we can truly drive a mutually profitable business model for enterprise customers while delivering Kryon Full-Cycle Automation, the industry’s most innovative solution with proven ROI.”

The new Kryon Channel Partners are:

• Agilify Automation (North America)
• Anna Partners (Israel)
• Cxrus Solutions, Pte. (Asia-Pacific)
• Indra Brazil (Latin America)
• KPMG (Israel)
• Matrix (Israel)
• ProVision IT Group (EMEA)
• Reveal Group (North America)
• Serikat (EMEA)

“Agilify is committed to ensuring our clients are successful with their intelligent automation journey,” said Doug Thompson, CEO of Agilify Automation. “We are looking forward to pairing Kryon’s technology with our practitioner expertise to assure our clients are reaching the most value and success with their automation investments.”

RPA is expected to have a breakthrough year in 2021, with some industry experts predicting nearly 20 percent growth globally compared to 2020. Value-added resellers and channel providers that are already selling enterprise technology to financial services, insurance, healthcare, hospitality, manufacturing, and telecom customers can expect increased demand for automation in the coming year.

“Kryon is a strategic addition to our digital operation practice, enabling Minsait to provide AI-based, Full-Cycle Automation to our customers in Brazil,” said Marcus Luz, Head of Digital Business & Executive Director at Minsait in Brazil, an Indra company.

Enterprise technology resellers can plan now to capture their share of this staggering revenue potential by attending the invite-only Kryon Partner Summit.

“This two-day, interactive summit will explore the latest use cases, inspire confidence in the value proposition of RPA, and provide expert training on Kryon’s latest innovations, including the industry’s first cloud delivery of Full-Cycle Automation,” said Daniel Peled, Senior Vice President of Channel Sales at Kryon.”

Image credit: Unsplash.

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