Dennis believes that every brand has the potential to inspire action, but it begins with knowing who you’re speaking to. “You can’t create the same message for everyone,” he explains. “It starts with truly understanding your audience—not just their demographics, but their aspirations, their desires, and what makes them light up.” This clarity is the cornerstone of Dennis’s storytelling process, enabling brands to move beyond generic marketing and into creating real, emotional connections.

Crafting a story that resonates isn’t about following a script; it’s about unlocking emotions that drive action. Dennis recalls a particularly powerful project with a nonprofit focused on substance abuse awareness. “That story was deeply emotional. We helped them communicate hope and resilience to their community,”. “It’s not just about showing what’s wrong—it’s about showing what’s possible.” 3 Bridges’ name was derived in part because of the strong belief that storytelling is a tool for building bridges—between brands and their customers, between challenges and solutions, and ultimately, between dreams and reality.

When brands feel stuck or unsure how to differentiate, 3 Bridges helps them break through by asking the right questions. “What makes your story unique? What’s the one thing your audience truly cares about?” Dennis probes. For startups, he emphasizes the importance of making a strong first impression. “You only get one chance to introduce yourself to your audience—make it count,” he advises. For established brands, he warns, “If you’re not investing in high-quality storytelling, your competitors will. And they’ll take your market share with them.”

Dennis encourages brands to embrace the blend of professional video production and user-generated content (UGC) but stresses the importance of strategy. “Don’t just create content for the sake of it. Focus on quality and purpose. High-quality videos not only elevate your brand but also build a content library that keeps working for you for months, even years,” he says. 3 Bridges recommends startups allocate 50% of their marketing budget to content creation, while established brands should aim for 20-30% depending on their goals. “It’s not about how much you spend—it’s about spending smartly. Always distribute content that aligns with your brand’s vision.”

Avoiding common pitfalls is crucial. “Playing it too safe is one of the biggest mistakes I see,” Dennis notes. “Safe content doesn’t inspire—it fades into the noise.” He also emphasizes the value of testing creative ideas before production. “Use tools like storyboards and even facial recognition software to gauge your audience’s reactions. This ensures your story will resonate before you even hit ‘record”, or go to market. 

At the heart of Dennis’s work is authenticity. In an industry where overpromising and underdelivering are common, Dennis and the team at 3 Bridges have built trust through transparency. “We’re upfront about what’s achievable. It’s not about making big promises—it’s about delivering real results,” he says. This approach has not only earned him loyal clients but has also set him apart as a trusted advisor in the industry.

As the landscape of storytelling evolves, Dennis embraces the possibilities of AI but keeps the human touch at the forefront. “AI is a tool, but it can’t replace creativity or emotional intelligence. Storytelling will always need the human element to truly connect,” he explains.

For Dennis Williams, storytelling is more than a profession—it’s a way to inspire growth, foster connections, and create lasting impact. Whether he’s helping a nonprofit amplify its mission,guiding startups to make their mark, or taking an established brand to the next level, Dennis proves that a well-told story isn’t just heard—it’s felt.If you’re ready to transform your brand’s narrative and create a strategy that resonates with your audience, 3 Bridges and Dennis Williams can help. Connect with him on LinkedIn or visit 3 Bridges Consulting to discover how storytelling can drive your success.

The post Behind the Lens: 3 Bridges Consulting’s Dennis Williams on Crafting Stories That Transform Brands appeared first on BusinessViewed.

Spencer Goffigan, a former Jax State Football athlete turned mental performance coach, is on a mission to help individuals unlock their full potential. Having overcome performance struggles himself, Spencer now works with athletes, guiding them to develop the mental resilience and focus needed to excel on and off the field. His passion stems from his own transformation, rising from one of the walk – on players on his team to becoming a team captain. Through his consulting coaching and podcast platform, Spencer is helping others harness the power of mindset and discipline to achieve success.

The Universal Power of Mental Performance

Spencer believes that the principles of mental performance extend far beyond sports. “Mental performance is universal,” he explains. “Whether you’re an athlete, a business leader, or someone striving for personal growth, it’s about discipline, routine, and performing confidently under pressure.” At the core of his coaching is a process that begins with understanding where individuals are, what they struggle with, and the habits that influence their mindset.

He emphasizes that success isn’t just about reaching goals—it’s about believing you are the type of person who can achieve them. “It’s not about fixating on how to conquer the entire Mount Everest. Instead, focus on climbing the next 500 feet and take it step by step,” he explains. By breaking down goals into manageable steps and building self-awareness, individuals can make consistent progress while learning to handle setbacks with grace.

Applying Mental Performance Beyond Sports

Spencer’s coaching principles resonate strongly with business leaders and professionals. “In business, just like in sports, you have an end goal—maybe hitting a revenue target or achieving a major milestone. The process is the same. You reverse-engineer the goal, break it down step by step, and build a routine to achieve it,” he shares.

He also emphasizes the importance of mental preparation in professional settings. “Skill alone isn’t enough. If you’re not mentally ready to handle pressure, especially when things go wrong, it’s hard to perform at your best,” Spencer says. He highlights that mental preparation often begins with self-awareness and positive self-talk. “Your self-talk shapes your reality. If it’s not positive, it needs to at least be productive.”

Building Resilience and Confidence

When setbacks occur, Spencer reframes failure as a learning opportunity. “It’s not winners and losers—it’s winners and learners. Every failure teaches you something if you’re willing to reflect on it,” he says. His approach involves guiding clients to analyze what went wrong, identify what to improve, and focus on what to continue doing.

Confidence, Spencer explains, is built through action. “Confidence doesn’t come from achieving something; it comes from doing. Whether it’s a penalty kick in soccer or a high-stakes business decision, act with confidence and let the results follow.”

Routine, Visualization, and Growth

Routine is a cornerstone of Spencer’s coaching philosophy. “From brushing your teeth to preparing for a big game or presentation, having a structured routine builds consistency and focus,” he says. Visualization is another powerful tool he uses to help clients achieve their goals. “Everything happens twice—first in your mind, then in reality. Visualization helps you mentally prepare for success before it happens.”

Spencer creates personalized mental imagery scripts for his clients, encouraging them to vividly imagine their performance environment. “For a baseball player, I’d ask about the temperature, specific senses, the sound of the crowd, and the feel of the ball in their hands. The goal is to immerse them in the moment of success.”

Challenges and Aspirations

One of the biggest challenges Spencer faces is spreading awareness about the impact of mental performance coaching. “Mental performance is still a new concept, and many people don’t fully understand its value,” he says. Through his website, podcast, and marketing efforts, he’s working to bridge this gap and reach more athletes.

Looking ahead, Spencer aspires to expand his coaching into the business world. “Athletes and business professionals face similar struggles—pressure, failure, and success. I want to help people in all walks of life realize that everything they need to achieve their goals is already in their mind. It’s just about unlocking it,” he shares.

Final Thoughts

Spencer Goffigan’s work is a testament to the transformative power of mindset and perseverance. Whether helping athletes develop resilience or envisioning a future in business coaching, his passion lies in empowering individuals to believe in themselves and take actionable steps toward their goals. As Spencer puts it, “Success isn’t just about reaching the goal—it’s about the process and journey; believing confidently you are the person to achieve the goal and consistently focusing on  the steps to get there.

The post Everything You Want to Achieve Is Already in Your Head—Spencer Goffigan on Unlocking Potential and Finding Success appeared first on BusinessViewed.

This is one of our top stories of 2024, as we look back on the biggest newsmakers of the year.

Bioceres Crop Solutions Corp. has just achieved a major milestone with the U.S. Department of Agriculture (USDA) giving the green light to its HB4 wheat technology.

The USDA’s favourable Regulatory Status Review (RSR), conducted by the Animal and Plant Health Inspection Service (APHIS), has determined that HB4 wheat does not pose any increased plant pest risk compared to conventional wheat. This ruling effectively clears the path for HB4 wheat production in the United States, which ranks as the fourth largest wheat producer globally and the largest in the Americas.

This USDA approval is a significant addition to the HB4 wheat platform, which had already received a nod from the U.S. Food and Drug Administration (FDA) for food and feed use. The U.S. now joins Argentina, Brazil, and Paraguay as the latest country to approve the production of HB4 wheat.

Beyond these regions, the technology has been authorized for food and feed use in countries including Australia, New Zealand, South Africa, Nigeria, and Indonesia. Australia, a key wheat-producing nation facing growing water shortages, recently granted Bioceres a license to conduct field trials — an essential step towards future production.

Bioceres will share more details on its HB4 wheat platform during its upcoming Earnings Call on Tuesday, Sept. 10 at 8:30 a.m. ET. This USDA approval could mark a pivotal moment for Bioceres as it seeks to solidify its presence in the global wheat market while addressing pressing agricultural challenges like drought tolerance and food security.

The post Best of 2024: The United States has Given the Green Light to GM Wheat appeared first on Seed World.

The post Best of 2024: The United States has Given the Green Light to GM Wheat appeared first on BusinessViewed.

If you’ve been watching Corteva Agriscience’s business strategy recently, you’ll have noted a change: Corteva has stepped into the licensing sphere. That means Corteva is able to extend its market presence, and it means a broader reach for its innovations. The seed value chain, from farmers right through to seed companies and beyond, want to know: what’s on the horizon for licensing? What can licensees expect in terms of new offerings from Corteva? So, we sat Corteva Agriscience’s head of licensing, Scott Schulz, down for a chat about why Corteva has a licensing business, what’s on the near-term and more distant schedule for licensing, and what he’s most excited about in 2025.  

The post Sneak Peek: What’s Coming in Licensing from Corteva Agriscience appeared first on Seed World.

The post Sneak Peek: What’s Coming in Licensing from Corteva Agriscience appeared first on BusinessViewed.

Artificial Intelligence (AI) is advancing at an extraordinary pace. What seemed like a futuristic concept just a decade ago is now part of our daily lives. However, the AI we encounter now is only the beginning. The fundamental transformation is yet to be witnessed due to the developments behind the scenes, with massive models capable of tasks once considered exclusive to humans. One of the most notable advancements is Hunyuan-Large, Tencent’s cutting-edge open-source AI model.

Hunyuan-Large is one of the most significant AI models ever developed, with 389 billion parameters. However, its true innovation lies in its use of Mixture of Experts (MoE) architecture. Unlike traditional models, MoE activates only the most relevant experts for a given task, optimizing efficiency and scalability. This approach improves performance and changes how AI models are designed and deployed, enabling faster, more effective systems.

The Capabilities of Hunyuan-Large

Hunyuan-Large is a significant advancement in AI technology. Built using the Transformer architecture, which has already proven successful in a range of Natural Language Processing (NLP) tasks, this model is prominent due to its use of the MoE model. This innovative approach reduces the computational burden by activating only the most relevant experts for each task, enabling the model to tackle complex challenges while optimizing resource usage.

With 389 billion parameters, Hunyuan-Large is one of the most significant AI models available today. It far exceeds earlier models like GPT-3, which has 175 billion parameters. The size of Hunyuan-Large allows it to manage more advanced operations, such as deep reasoning, generating code, and processing long-context data. This ability enables the model to handle multi-step problems and understand complex relationships within large datasets, providing highly accurate results even in challenging scenarios. For example, Hunyuan-Large can generate precise code from natural language descriptions, which earlier models struggled with.

What makes Hunyuan-Large different from other AI models is how it efficiently handles computational resources. The model optimizes memory usage and processing power through innovations like KV Cache Compression and Expert-Specific Learning Rate Scaling. KV Cache Compression speeds up data retrieval from the model’s memory, improving processing times. At the same time, Expert-Specific Learning Rate Scaling ensures that each part of the model learns at the optimal rate, enabling it to maintain high performance across a wide range of tasks.

These innovations give Hunyuan-Large an advantage over leading models, such as GPT-4 and Llama, particularly in tasks requiring deep contextual understanding and reasoning. While models like GPT-4 excel at generating natural language text, Hunyuan-Large’s combination of scalability, efficiency, and specialized processing enables it to handle more complex challenges. It is adequate for tasks that involve understanding and generating detailed information, making it a powerful tool across various applications.

Enhancing AI Efficiency with MoE

More parameters mean more power. However, this approach favors larger models and has a downside: higher costs and longer processing times. The demand for more computational power increased as AI models grew in complexity. This led to increased costs and slower processing speeds, creating a need for a more efficient solution.

This is where the Mixture of Experts (MoE) architecture comes in. MoE represents a transformation in how AI models function, offering a more efficient and scalable approach. Unlike traditional models, where all model parts are active simultaneously, MoE only activates a subset of specialized experts based on the input data. A gating network determines which experts are needed for each task, reducing the computational load while maintaining performance.

The advantages of MoE are improved efficiency and scalability. By activating only the relevant experts, MoE models can handle massive datasets without increasing computational resources for every operation. This results in faster processing, lower energy consumption, and reduced costs. In healthcare and finance, where large-scale data analysis is essential but costly, MoE’s efficiency is a game-changer.

MoE also allows models to scale better as AI systems become more complex. With MoE, the number of experts can grow without a proportional increase in resource requirements. This enables MoE models to handle larger datasets and more complicated tasks while controlling resource usage. As AI is integrated into real-time applications like autonomous vehicles and IoT devices, where speed and low latency are critical, MoE’s efficiency becomes even more valuable.

Hunyuan-Large and the Future of MoE Models

Hunyuan-Large is setting a new standard in AI performance. The model excels in handling complex tasks, such as multi-step reasoning and analyzing long-context data, with better speed and accuracy than previous models like GPT-4. This makes it highly effective for applications that require quick, accurate, and context-aware responses.

Its applications are wide-ranging. In fields like healthcare, Hunyuan-Large is proving valuable in data analysis and AI-driven diagnostics. In NLP, it is helpful for tasks like sentiment analysis and summarization, while in computer vision, it is applied to image recognition and object detection. Its ability to manage large amounts of data and understand context makes it well-suited for these tasks.

Looking forward, MoE models, such as Hunyuan-Large, will play a central role in the future of AI. As models become more complex, the demand for more scalable and efficient architectures increases. MoE enables AI systems to process large datasets without excessive computational resources, making them more efficient than traditional models. This efficiency is essential as cloud-based AI services become more common, allowing organizations to scale their operations without the overhead of resource-intensive models.

There are also emerging trends like edge AI and personalized AI. In edge AI, data is processed locally on devices rather than centralized cloud systems, reducing latency and data transmission costs. MoE models are particularly suitable for this, offering efficient processing in real-time. Also, personalized AI, powered by MoE, could tailor user experiences more effectively, from virtual assistants to recommendation engines.

However, as these models become more powerful, there are challenges to address. The large size and complexity of MoE models still require significant computational resources, which raises concerns about energy consumption and environmental impact. Additionally, making these models fair, transparent, and accountable is essential as AI advances. Addressing these ethical concerns will be necessary to ensure that AI benefits society.

The Bottom Line

AI is evolving quickly, and innovations like Hunyuan-Large and the MoE architecture are leading the way. By improving efficiency and scalability, MoE models are making AI not only more powerful but also more accessible and sustainable.

The need for more intelligent and efficient systems is growing as AI is widely applied in healthcare and autonomous vehicles. Along with this progress comes the responsibility to ensure that AI develops ethically, serving humanity fairly, transparently, and responsibly. Hunyuan-Large is an excellent example of the future of AI—powerful, flexible, and ready to drive change across industries.

The post Hunyuan-Large and the MoE Revolution: How AI Models Are Growing Smarter and Faster appeared first on Unite.AI.

The post Hunyuan-Large and the MoE Revolution: How AI Models Are Growing Smarter and Faster appeared first on BusinessViewed.

As the demand for generative AI grows, so does the hunger for high-quality data to train these systems. Scholarly publishers have started to monetize their research content to provide training data for large language models (LLMs). While this development is creating a new revenue stream for publishers and empowering generative AI for scientific discoveries, it raises critical questions about the integrity and reliability of the research used. This raises a crucial question: Are the datasets being sold trustworthy, and what implications does this practice have for the scientific community and generative AI models?

The Rise of Monetized Research Deals

Major academic publishers, including Wiley, Taylor & Francis, and others, have reported substantial revenues from licensing their content to tech companies developing generative AI models. For instance, Wiley revealed over $40 million in earnings from such deals this year alone​. These agreements enable AI companies to access diverse and expansive scientific datasets, presumably improving the quality of their AI tools.

The pitch from publishers is straightforward: licensing ensures better AI models, benefitting society while rewarding authors with royalties. This business model benefits both tech companies and publishers. However, the increasing trend to monetize scientific knowledge has risks, mainly when questionable research infiltrates these AI training datasets.

The Shadow of Bogus Research

The scholarly community is no stranger to issues of fraudulent research. Studies suggest many published findings are flawed, biased, or just unreliable. A 2020 survey found that nearly half of researchers reported issues like selective data reporting or poorly designed field studies. In 2023, more than 10,000 papers were retracted due to falsified or unreliable results, a number that continues to climb annually. Experts believe this figure represents the tip of an iceberg, with countless dubious studies circulating in scientific databases​.

The crisis has primarily been driven by “paper mills,” shadow organizations that produce fabricated studies, often in response to academic pressures in regions like China, India, and Eastern Europe. It’s estimated that around 2% of journal submissions globally come from paper mills. These sham papers can resemble legitimate research but are riddled with fictitious data and baseless conclusions. Disturbingly, such papers slip through peer review and end up in respected journals, compromising the reliability of scientific insights​. For instance, during the COVID-19 pandemic, flawed studies on ivermectin falsely suggested its efficacy as a treatment, sowing confusion and delaying effective public health responses. This example highlights the potential harm of disseminating unreliable research, where flawed results can have a significant impact.

Consequences for AI Training and Trust

The implications are profound when LLMs train on databases containing fraudulent or low-quality research. AI models use patterns and relationships within their training data to generate outputs. If the input data is corrupted, the outputs may perpetuate inaccuracies or even amplify them. This risk is particularly high in fields like medicine, where incorrect AI-generated insights could have life-threatening consequences.
Moreover, the issue threatens the public’s trust in academia and AI. As publishers continue to make agreements, they must address concerns about the quality of the data being sold. Failure to do so could harm the reputation of the scientific community and undermine AI’s potential societal benefits.

Ensuring Trustworthy Data for AI

Reducing the risks of flawed research disrupting AI training requires a joint effort from publishers, AI companies, developers, researchers and the broader community. Publishers must improve their peer-review process to catch unreliable studies before they make it into training datasets. Offering better rewards for reviewers and setting higher standards can help. An open review process is critical here. It brings more transparency and accountability, helping to build trust in the research.
AI companies must be more careful about who they work with when sourcing research for AI training. Choosing publishers and journals with a strong reputation for high-quality, well-reviewed research is key. In this context, it is worth looking closely at a publisher’s track record—like how often they retract papers or how open they are about their review process. Being selective improves the data’s reliability and builds trust across the AI and research communities.

AI developers need to take responsibility for the data they use. This means working with experts, carefully checking research, and comparing results from multiple studies. AI tools themselves can also be designed to identify suspicious data and reduce the risks of questionable research spreading further.

Transparency is also an essential factor. Publishers and AI companies should openly share details about how research is used and where royalties go. Tools like the Generative AI Licensing Agreement Tracker show promise but need broader adoption. Researchers should also have a say in how their work is used. Opt-in policies, like those from Cambridge University Press, offer authors control over their contributions. This builds trust, ensures fairness, and makes authors actively participate in this process.

Moreover, open access to high-quality research should be encouraged to ensure inclusivity and fairness in AI development. Governments, non-profits, and industry players can fund open-access initiatives, reducing reliance on commercial publishers for critical training datasets. On top of that, the AI industry needs clear rules for sourcing data ethically. By focusing on reliable, well-reviewed research, we can build better AI tools, protect scientific integrity, and maintain the public’s trust in science and technology.

The Bottom Line

Monetizing research for AI training presents both opportunities and challenges. While licensing academic content allows for the development of more powerful AI models, it also raises concerns about the integrity and reliability of the data used. Flawed research, including that from “paper mills,” can corrupt AI training datasets, leading to inaccuracies that may undermine public trust and the potential benefits of AI. To ensure AI models are built on trustworthy data, publishers, AI companies, and developers must work together to improve peer review processes, increase transparency, and prioritize high-quality, well-vetted research. By doing so, we can safeguard the future of AI and uphold the integrity of the scientific community.

The post Monetizing Research for AI Training: The Risks and Best Practices appeared first on Unite.AI.

The post Monetizing Research for AI Training: The Risks and Best Practices appeared first on BusinessViewed.

Slovakia-based battery maker InoBat has raised €100 million in equity funding. The investment came from strategic investors Amara Raja and Rio Tinto, with significant contributions from SIH (Slovakia’s sovereign wealth fund), Lilium, Bromo Capital, IPM Group, and Cielo Capital. 

Gotion, the Chinese battery cell maker, backed by the Volkswagen Group, also cements its partnership with InoBat by becoming a cornerstone investor in the round. 

This investment has hit the headlines less than a month after Northvolt, the Swedish maker of battery cells for electric vehicles, filed for Chapter 11 bankruptcy protection in the US due to concerns about the future of Europe’s EV battery industry.

What’s next for InoBat?

Over the next year, InoBat aims to ramp up production of European-designed battery cells, start an energy storage business in partnership with Gotion, and launch another investment round to support the scaling of its operations in Slovakia, Serbia, and Spain and accelerating growth in new regions. 

Makes high-performance NMC battery cells 

InoBat was founded by Marian Bocek, Faysal Sohail, Jozef Urban, and Marian Goga in 2019. The company released its latest battery cell technology, demonstrating promising results in testing with customers. The company’s facilities include research and development laboratories, a semi-automatic pilot line, and an integrated laboratory. InoBat’s batteries are designed to be fully variable and rapidly adaptable.

Milestones achieved 

InoBat is determined to overcome the challenges facing Europe’s battery industry and remain a leader in sustainable energy solutions. Its milestones include the following: 

Volta 1, InoBat’s R&D and low-volume, high-performance battery manufacturing facility in Volderady became fully operational with 150 people from 19 nationalities combining to make Europe’s only high silicon anode cell. The advancement of the GIB (Gotion InoBat Batteries) gigafactory in Šurany, Slovakia. This state-of-the-art facility is set to address Europe’s rising demand for safe, sustainable batteries. 

CEO and Co-founder Marián Boček said: “As we reflect on 2024, I am incredibly proud of our achievements in technology, partnerships, and global expansion. In 2025, we will continue striving to become Europe’s leading battery company, delivering high-performance and sustainable solutions for the energy transition. I believe this round, the largest of its kind in for a Slovak technology company will pave the way for Central Europe’s circular battery ecosystem – Danube Valley.”

Chairman Dr Andy Palmer CMG: “Western Europe has been slow to react to the critical need for battery technology. Inobat has quietly gone about building both its own high-performance cell technology and its pragmatic partnership with Gotion to produce cost-effective cells. The journey has still a long way to go, but closing Series C and moving to a scaling agenda with Series D is an important milestone. In 2025, we will continue to advance the science of batteries in Europe.”

The post InoBat snaps €100M to supercharge European EV battery industry appeared first on Tech Funding News.

The post InoBat snaps €100M to supercharge European EV battery industry appeared first on BusinessViewed.

NorthStar Genetics has acquired Dyck Forages & Grasses.

“We are very excited to enter into the forage and grass seed business with the Dyck Forages & Grasses team in Elie, Man.” says Travis Williams, CEO of NorthStar Genetics. “This is a wonderful opportunity to expand and diversify our business while allowing us to stay focused on our customers and core strengths.”

Founder of Dyck Forages & Grasses, David Dyck comments, “It is very encouraging to see a locally owned company investing in the forage and grass seed business in Western Canada. Our customers, seed growers and team members will be in very good hands with NorthStar Genetics.”

“Dyck Forages & Grasses has successfully served customers around the world for over 36 years. Our NorthStar Genetics team is humbled and eager to join forces with David and team to build on these successes,” Williams adds. “Forage and grass seed complements our soybean and corn seed business.”

Dyck Forages & Grasses will operate as a division of NorthStar Genetics. NorthStar Genetics will continue to have headquarters in Domain, Man.

The post NorthStar Genetics Acquires Dyck Forages & Grasses appeared first on Seed World.

The post NorthStar Genetics Acquires Dyck Forages & Grasses appeared first on BusinessViewed.

The EU-funded Horizon 2020 INVITE project concluded its five-and-a-half-year journey with a final conference on Dec. 10 in Brussels. 

Jointly organized with its sister project, InnoVar, the event brought together researchers, government authorities, plant breeders, policymakers, and stakeholders to celebrate the projects’ achievements and explore the future of plant breeding and agricultural sustainability, according to a press release. Hosted by Arcadia International with support from Euroseeds, the conference marked a significant milestone in plant variety testing, with all project outputs now available online.

Key Achievements of the INVITE Project

Under the coordination of François Laurens from INRAE, the INVITE project has made substantial progress in enhancing the efficiency and quality of variety testing for a diversity of crops such as apple, fodder grass, wheat, sunflower, soybean, maize, potato, tomato, oil seed rape, and lucerne, which resulted in the following three notable achievements:

• Development of bioindicators for climate resilience
• Introduction of advanced phenotyping and genotyping tools
• Recommendations for improving variety testing performance

The conference showcased key advancements, including trait-specific markers for tomatoes to enhance variety identification and a reference collection for heterogeneous crops. Cutting-edge phenotyping techniques were highlighted, such as drone-based measurement of plant height and biomass in ryegrass and a mobile app for evaluating tomato fruit characteristics. By integrating genomics, phenomics, and machine learning, these innovations aim to accelerate the development and commercialization of improved, competitive plant varieties for European agriculture.

Collaboration with the Sister Project InnoVar on Wheat

The InnoVar project presented its groundbreaking innovations, particularly in the testing of wheat varieties. By collaborating with INVITE, both projects showcased the potential for integrating their tools and methodologies to maximize impacts for plant variety testing.

Keynote Address by MEP Herbert Dorfmann About Legislative Actions

Mr. Herbert Dorfmann, Member of the European Parliament and rapporteur for the legislative proposal on plant reproductive material production and marketing in the EU, delivered a keynote address at the event. He emphasized the critical role of sustainable agriculture in ensuring food security amid global challenges. “Agriculture has never been as crucial as it is today,” Dorfmann remarked, underscoring how the revised EU seed marketing legislation aims to balance innovation with sustainability.

Multi-Stakeholder Panel Discussion on the Future of Plant Variety Testing

A key moment of the conference was the stakeholder panel, moderated by Jean-Paul Judson (NOWMORE), which brought together diverse perspectives from policymakers, industry leaders, and researchers. Panelists included Herbert Dorfmann (European Parliament), Philippe Lesigne (Bayer), Thor Kofoed (Copa-Cogeca), Jean Maison (Community Plant Variety Office), Monika Messmer (Research Institute of Organic Agriculture FiBL), Päivi Mannerkorpi (European Commission), François Laurens (INRAE), and Lisa Black (Agri-Food and Biosciences Institute).

Key elements from the panel discussion:

• Future-proof legislation: The new EU plant reproductive material (PRM) legislative proposal aims to balance innovation with sustainability, fostering resilience and adaptability in plant breeding. Päivi Mannerkorpi noted, “It is important to look not only at productivity but also at the resilience of a variety, which contributes to sustainability.”
• Sustainability in variety testing: Thor Kofoed emphasized the need for information provided to farmers about sustainability traits, and Philippe Lesigne called all relevant actors to aim for a common understanding and hence alignment of sustainability characteristics to enable transformative change in plant variety testing and to foster competitive agriculture in Europe.
• Fast-tracking innovation: Accelerated breeding cycles and cost-efficient testing were deemed critical to bringing faster resilient varieties to the farming community in Europe. Jean Maison remarked, “The time and cost efficiency are very important to advance the system.”
• Global outlook: Sustainability is a global priority requiring unified approaches. Philippe Lesigne stressed the importance of leveraging INVITE as a pilot to better understand relevant characteristics for evaluation.
• Organic sector: Monika Messmer underscored that improved harmonization and flexibility in the PRM legislative proposal is needed for varieties that could be used in organic agriculture.
• Enhanced collaboration: Exchanges among researchers, farmers, breeders, and examination offices are deemed essential for fostering innovation in variety plant variety testing and advancing concrete solutions to the benefit of agriculture and society.

Concluding Remarks

The coordinator François Laurens shared his reflections on INVITE’s achievements, including the development of a variety testing database prototype, tools tailored to Europe’s diverse agricultural systems and the collaboration with InnoVar. Laurens noted that the diversity of outputs will have a long-lasting impact on plant variety testing in Europe, paving the way for a more sustainable and resilient agricultural future.

Watch the Seed World Europe video interview: Where is Variety Testing Headed? A Closer Look at the INVITE Project

The post INVITE Final Conference: Future Innovations in Plant Variety Testing appeared first on Seed World.

The post INVITE Final Conference: Future Innovations in Plant Variety Testing appeared first on BusinessViewed.

UK-based Mindgard, specialising in AI security testing, has secured $8 million in funding. .406 Ventures led the round with participation from Atlantic Bridge, Willowtree Investments, and existing investors IQ Capital and Lakestar (which recently backed Dexory and Vsim). 

The funding will help the company build the team of 15 by the end of next year, product development and R&D, and expand into the US.

New appointees

In addition to the investment, Mindgard appointed two new executives, Dave Ganly, a former Director of Product at Twilio, and Fergal Glynn, who most recently served as CMO at Next DLP (acquired by Fortinet). They will play a critical role in the company’s product development and launch Mindgard’s expansion into the North American market with a leadership presence in Boston.

Need for security in AI

The deployment and use of AI introduce new risks, creating a complex security landscape that traditional tools cannot address. As a result, many AI products are being launched without adequate security assurances, leaving organisations vulnerable. This is an issue underscored by a Gartner finding that 29% of enterprises deploying AI systems have reported security breaches, and only 10% of internal auditors have visibility into AI risk.

Many of these new risks such as LLM prompt injection and jailbreaks exploit AI systems’ probabilistic and opaque nature, which only manifests at runtime. Securing these risks, unique to AI models and toolchains, requires a fundamentally new approach.

Mindgard revolutionises AI security testing and automated AI red teaming with its Dynamic Application Security Testing for AI (DAST-AI) solution.

Protects companies from AI threats

Founded in 2022 by Dr. Peter Garraghan and Steve Street, Mindgard’s technology identifies and resolves AI-specific vulnerabilities that can only be detected during runtime. For organisations adopting AI or establishing guardrails, continuous security testing is essential for gaining risk visibility across the AI lifecycle.

Mindgard’s solution integrates into existing automation, empowering security teams, developers, AI red teamers, and pentesters to secure AI without disrupting established workflows.

“The rapid adoption of AI has introduced new and complex security risks that traditional tools cannot address,” said Greg Dracon, Partner at .406 Ventures. “Mindgard’s innovative approach, born out of the distinct challenges of securing AI, equips security teams and developers with the tools they need to deliver secure AI systems. Mindgard is well-positioned to lead this emerging market and we are thrilled to partner with them on this journey.”

“All software has security risks, and AI is no exception,” said Dr. Peter Garraghan, CEO of Mindgard and Professor at Lancaster University. “The challenge is that the way these risks manifest within AI is fundamentally different from other software. Drawing on our 10 years of experience in AI security research, Mindgard was created to tackle this challenge. We’re proud to lead the charge toward creating a safer, more secure future for AI.”

The post UK-based Mindgard raises $8M to protect companies from AI vulnerabilities appeared first on Tech Funding News.

The post UK-based Mindgard raises $8M to protect companies from AI vulnerabilities appeared first on BusinessViewed.