The UK edtech unicorn Multiverse, which builds apprenticeship programs, has acquired Searchlight, a talent intelligence and skills assessment platform that uses AI to help companies close their skills gaps.

California-based Searchlight was founded in 2018 by twins Anna Wang and Kerry Wang. The Stanford graduates started their journey with Y Combinator, and have since been recognized by Forbes 30 under 30. The company also received investment from top venture capital firms including Founders Fund and Accel.

Accelerating AI implementation at Multiverse

The combination of Searchlight’s technology and talent will accelerate the use of generative AI and machine learning at Multiverse. Searchlight’s custom-built and bias-free AI engine will enhance Multiverse’s ability to identify, analyze, and close skills gaps within organizations through programs that are closely mapped to customer business needs, the company stated. 

Multiverse partners with more than 1,000 organizations to deliver training in areas such as data analytics and software engineering. Its training model is built on a new kind of apprenticeship that is measured, applied, guided, and equitable, and its tech platforms exist to ensure learners have access to a high-quality, personalized experience that delivers measured ROI for their employers.

Searchlight is the second acquisition for the company, following the purchase of Eduflow, another Y Combinator company, in May 2023.

The Searchlight team will join Multiverse to spearhead the integration of its talent intelligence technology into Multiverse’s platform and lead Multiverse’s AI initiatives. Searchlight’s CEO, Kerry Wang will become Director of Product. Searchlight’s CTO, Anna Wang, will become Head of AI.

Founder and CEO of Multiverse, Euan Blair, said: “After meeting Anna and Kerry and digging into the Searchlight product, I was really excited at how they were using AI to spot patterns and identify skilling solutions both within and outside of the workforce. Most companies are on a journey of tech transformation and they want to do it in a way that is both equitable and effective. What often holds them back is the gap between the transformation they want to see, and the skills that will unlock it.” 

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AWS announced the AWS Generative AI Innovation Center, a new program to help customers successfully build and deploy generative artificial intelligence (AI) solutions. 

AWS is investing $100 million in the program, which will connect AWS AI and machine learning (ML) experts with customers around the globe to help them envision, design, and launch new generative AI products, services, and processes. 

The AWS Generative AI Innovation Center team of strategists, data scientists, engineers, and solutions architects will work with customers to build bespoke solutions that harness the power of generative AI. For example, healthcare and life sciences companies can pursue ways to accelerate drug research and discovery. Manufacturers can build solutions to reinvent industrial design and processes. And financial services companies can develop ways to provide customers with more personalized information and advice.

“The Generative AI Innovation Center is part of our goal to help every organization leverage AI by providing flexible and cost-effective generative AI services for the enterprise, alongside our team of generative AI experts to take advantage of all this new technology has to offer. Together with our global community of partners, we’re working with business leaders across every industry to help them maximize the impact of generative AI in their organizations, creating value for their customers, employees, and bottom line,” said Matt Garman, senior vice president of Sales, Marketing, and Global Services at AWS. 

Through no cost workshops, engagements, and training, AWS will help customers imagine and scope the use cases based on best practices and industry expertise. Customers will work closely with generative AI experts from AWS and the AWS Partner Network to select the right models, define paths to navigate technical or business challenges, develop proofs of concepts, and make plans for launching solutions at scale. 

Engagements will deliver strategy, tools, and assistance that will help customers use AWS generative AI services through APIs. Some of these services include Amazon CodeWhisperer and Amazon Bedrock, a fully managed service that makes foundational models (FMs) from AI21 Labs, Anthropic, and Stability AI, along with Amazon’s own family of FMs, Amazon Titan.

 They can also train and run their models using high-performance infrastructure, including AWS Inferentia-powered Amazon EC2 Inf1 Instances, AWS Trainium-powered Amazon EC2 Trn1 Instances, and Amazon EC2 P5 instances powered by NVIDIA H100 Tensor Core GPUs. Additionally, customers can build, train, and deploy their own models with Amazon SageMaker or use Amazon SageMaker Jumpstart to deploy some of today’s most popular FMs, including Cohere’s large language models, Technology Innovation Institute’s Falcon 40B, and Hugging Face’s BLOOM.

Companies like Highspot, Lonely Planet and Twilio are among the AWS customers who are in the process of leveraging the services offered by the innovation center. 

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A majority of employers around the world are optimistic that emerging technologies, including artificial intelligence (AI), machine learning, virtual/augmented reality (VR/AR), blockchain, and others, won’t eliminate workers, but will actually create more jobs. That’s according to The Future is Now: AI, the Metaverse, & the World of Work, from Experis, the global IT professional resourcing and services company that is part of the ManpowerGroup family of brands.

“The integration of AI, machine learning, VR/AR, and other emerging technologies is rapidly transforming industries and driving the need for an adaptable workforce,” said Experis Senior Vice President Ger Doyle. “We are seeing companies embrace these new technologies with many seeking to hire or upskill existing talent to take advantage of potential productivity gains. Smart employers know that embracing digitization and nurturing human talent will enhance their readiness to succeed in this era of rapid technological advancement.”

With 78% of IT organizations reporting difficulty hiring the talent they need with the skills they covet, an increasing number of companies are either adopting or planning to adopt the use of emerging technologies in their recruiting processes, including: AI (35% adopted, 36% plan to), Conversational AI (35% adopted, 36% plan to), Machine Learning (38% adopted, 34% plan to), and VR (30% adopted, 35% plan to), according to ManPower Group Talent Shortage Survey.

ManpowerGroup interviewed 5,978 IT employers across 41 countries on hiring intentions for the third quarter of 2023.

“So far, the signs are no different from what we have seen with earlier versions of AI or tech innovation. Generative AI can be expected to mostly automate tasks and skills within jobs rather than entire jobs. We should relish the opportunity to outsource these mundane tasks – freeing up our time for more creative and intellectually sophisticated endeavors,” ManpowerGroup Chief Innovation Officer Tomas Chamorro-Premuzic said. “This isn’t about us versus AI or humans versus machines. Instead, it’s about how we can leverage these tools to augment and upgrade our uniquely human skills and lead a more human-centric life.”

Additionally, the latest Experis Tech Talent Outlook finds the rise of new tech has also shifted the hiring priorities and focus for employers.

The top five staffing priorities reported are:

1. Cybersecurity (34%)
2. Technical Support (32%)
3. Customer or User Experience (31%)
4. Database Management (27%)
5. Customer Relationship Management Systems (25%)

To address these technology-related challenges, employers shared they are implementing the following approaches:

• Training and upskilling the current workforce (50%)
• Hiring new workers with the required skills (46%)
• Investing more in automation (39%)
• Reskilling workers and transitioning them into IT roles (39%)
• Hiring short-term staff (freelancers, contract workers, etc.) to fill skills gaps (38%)

The Experis Tech Talent Outlook research is based on results from the ManpowerGroup Employment Outlook Survey.

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In the fight against money laundering, established software-based detection methods work imprecisely and often raise false alarms. Consequently, investigating authorities are frequently overburdened because they must follow up on every suspicion.

In a new research project MaLeFiz (Machine Learning for the Identification of Conspicuous Financial Transactions), researchers are now working on a solution that uses machine learning to improve the search for illegal money flows and make it more precise so that fewer false alarms are generated.

In addition, the project partners are developing minimum requirements and control mechanisms for AI solutions used in the financial industry. Furthermore, the results of the AI are to be made traceable. Project partners are Deloitte GmbH, the Fraunhofer Institute for Secure Information Technology SIT, the Martin Luther University Halle-Wittenberg, the University of Leipzig as well as the Center for Technology and Society at TU Berlin. The MaLeFiz project is funded by the German Federal Ministry of Education and Research and will run for three years. Fraunhofer SIT is leading the project and is responsible for the development of the AI-based tool.

The analysis of financial transactions is an important component in the fight against money laundering. However, current analysis methods identify too many suspicious cases, all of which must be verified individually by specifically trained analysts. The responsible authority, the Financial Intelligence Unit or FIU for short, received around 300,000 reports per year in the past two years alone and currently still has around 290,000 not finally processed alerts. Artificial intelligence methods promise better analysis options, which means fewer false positives. The research team is using machine learning methods to develop the necessary technical solution.

For the results of such analyses to stand up in court, the IT solutions must meet certain requirements. For example, the decisions of an AI must be comprehensible: The AI should not be a “black box” and simply spit out a list of suspicious cases.

The demonstrator, the catalog of minimum requirements, and other project results will be made available to the public after completion of the project in September 2025.

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