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Google announced its widely anticipated Gemini 3 model Tuesday. By many key metrics, it appears to be more capable than the other big generative AI models on the market. In a show of confidence in the performance (and safety) of the new model, Google is making one variant of GeminiGemini 3 Proavailable to everyone via the Gemini app starting now. Its also making the same model a part of its core search service for subscribers. The new model topped the scores of the much-cited LMArena benchmark, a crowdsourced preference of various top models based on head-to-head responses to identical prompts. In the super-difficult Humanitys Last Exam benchmark test, which measured reasoning and knowledge, the Gemini 3 Pro scored 37.4% compared to GPT-5 Pros 31.6%. Gemini 3 also topped a range of other benchmarks measuring everything from reasoning to academic knowledge to math to tool use and agent functions. Gemini has been a multimodal model from the start, meaning that it can understand and reason about not just language, but images, audio, video, and codeall at the same time. This capability has been steadily improving since the first Gemini, and Gemini 3 reached state-of-the-art performance on the MMMU-Pro benchmark, which measures how well a model handles college-level and professional-level reasoning across text and images. It also topped the Video-MMMU benchmark, which measures the ability to reason over details of video footage. For example, the Gemini model might ingest a number of YouTube videos, then create a set of flashcards based on what it learned. Gemini also scored high on its ability to create computer code. Thats why it was a good time for the company to launch a new Cursor-like coding agent called Antigravity. Software development has proven to be among the first business functions in which generative AI has had a measurably positive impact. Benchmarks are telling, but as the response to OpenAIs GPT-5.1 showed, the feel or personality of a model matters to users (many users thought GPT-5 was a dramatic personality downgrade from GPT-4o). Google DeepMind CEO Demis Hassabis seemed to acknowledge this in a tweet Tuesday. [B]eyond the benchmarks its been by far my favorite model to use for its style and depth, and what it can do to help with everyday tasks. Of course users will have their own say about Gemini 3s communication style, and how well it adapts to user preferences and work habits. With the release of Googles third-generation generative AI model, its a good time to look at the wider context of the race to build the dominant AI models of the 21st century. The contest, remember, is only a few years old. So far, OpenAIs models have spent the most time atop the benchmark rankings, and, on the strength of ChatGPT, have garnered most of the attention of all the players in the emerging AI industry. History on its side? From the start, Google has enjoyed some distinct advantages. Its been investing in AI talent and research for decades, starting long before OpenAI became a company in 2015. It began developing machine learning techniques for understanding search intent, defining page rank, and for placing ads as far back as 2001. It bought London-based AI research lab DeepMind back in 2014, and DeepMind has been responsible for some of Googles biggest AI accomplishments (AlphaGo, AlphaFold, Gemini models). The big research breakthroughs that enabled the current wave of generative AI models took place at Google. In 2017, Google researchers invented the transformer language model architecture that allowed LLMs to learn much more from their training data than earlier language models. The following year Google used the transformer architecture to build its BERT language model, which led directly to the GPT models that power ChatGPT. In fact, the search giant developed an AI chatbot well before OpenAI did, but was conflicted about releasing it or infusing it into its other products because of legal and business model concerns. All the data Google has access to more and better-quality training data than any other AI company. Its been indexing most of the information on the web since 1998. It also owns huge amounts of information such as local business data, mapping data, and customer reviews, which can be used to train AI models or augment their output (within search results, for example). Generative models are just now gaining the ability to learn about the world from video footage in the same way that models learn from large amounts of text. With YouTube, Google has access to mountains of it, and its AI models could gain an increasing intelligence advantage by training on it. As AI begins to manage more and more of our personal and work tasks, Googles advantages in experience, talent, and data and other resources may help sustain Geminis state-of-the-art status and overall functionality in the years to come. High stakes This is more than about which company can sell the most API access to its models or subscriptions to a chatbot. As models like Gemini, Claude, and GPT-5 may eventually become smarter, perhaps far smarter, than humans at almost any task. The company with the models that reaches that level, also called artificial general intelligence (AGI) may dominate the marketplace for consumer and business AI in the same way Google has dominated search in the first decades of this century. With tech companies already spending hundreds of billions to build the infrastructure for their AI businesses, the pressure is mounting to push harder and faster on the development of new generations of AI models.
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E-Commerce
AI is bringing voice to the forefront of brand interactions. Smarter AI means we can talk to our technologyLLMs, software, phones, cars, fridges, and even banking apps. The novel part is this: Our technology is now talking back, and convincingly so. Brands are catching on, and the smart ones know that voice isnt just functional, it will form a core part of the brand identity itself. Voice will be the next frontier of branding. And not metaphorically. A brands literal voicethe voice(s) used for advertising, on their website, and now, in interactive AI-based conversations with customersis becoming just as ownable as elements of a visual identity. But standing out wont come from just using voice tech alone. To cut through the noise, brands will need a voice thats authentic, distinct, and is uniquely associated with their brand. The biggest brands already understand this. Theres a reason the most memorable brands choose to use the same voice actor across marketing campaigns, sometimes even across years: Consistency builds memorability, recognition, and trust. With voice AI, the opportunity for consistency and impact is even greater, and brands that embrace it will set themselves apart from the rest. TURN CUSTOMER TOUCHPOINTS INTO BRANDED EXPERIENCES The real gold in voice AI is its ability to provide both one-to-one and one-to-many communication at scale. AI is empowering brands to automate interactions across more customer touchpoints than ever before, including sales support, call center automations, and personalized ads, to name a few. As these channels incorporate voice AI, the need for consistency grows, making a singular, distinct voice more critical than ever. With voice AI, brands can hold a million individual conversations at once while maintaining both continuity and a personal touch. In customer support, an AI-powered agent can provide instant answers and even act via voice. That same voice can guide them through a product tutorial, help pay a phone bill, or introduce your brand to customers in an ad. Thats the beauty of voice AI as a brand asset: One voice can now efficiently scale, enabling a whole new level of brand cohesion across multiple interactions. Customers value predictability, and a consistent, trusted, and recognizable voice can really drive home that brand memorability and distinction. SECURE A MEMORABLE VOICE THATS EXCLUSIVELY YOURS With technology moving so fast, theres no shortage of ready-to-go AI voices. But the convenience of these voices doesnt guarantee exclusivity, and in branding, distinction is everything. The problem with 100% synthetic AI voicesvoices entirely created with AI, with no real human in the loopis threefold: They may become unavailable. They are often forgettable. They are rarely exclusive to the user. As vendors update their library or licenses expire, the voice youve been using to represent your brand could change, or even completely disappear. Even if it doesnt, chances are: Other brands and creators are using that same off-the-shelf voice, erasing any sense of individuality. As a brand, youll want at least some exclusivity for your AI voice, so you dont end up sharing it with a competitor. The reality is, the best AI voice clones come from real humans with the best voices: voice actors. You can hear a tangible difference between a synthetic AI voice and an AI voice cloned from a skilled voice talent. Done right, the one-to-one voice clone is higher quality than any synthetic voicenot only in its realism, but in its emotional nuance, uniqueness, and overall human quality. Licensing a professional voice also gives you greater control over creative direction to ensure the pronunciation of brand names and technical terms is correct. Licensed voices also offer customizable licensing suited to your specific needs, securing long-term consistency, exclusivity, and greater legal protections. Its the difference between borrowing something generic and curating a voice experience that is yours. The best, most successful branded voices in the market today are distinct and emotive. Customers wont remember an AI chatbot with a friendly middle-aged female voice, but they will remember a voice with personalityone that feels alive, intentional, and unmistakably part of the brand. Thats the future: Voice as a distinguishable brand asset, just like a logo. And by working with real humans to create a unique AI voice, youll secure something competitors cant copy: A voice that is exclusively, recognizably, and enduringly yours. Jay OConnor is CEO of Voices.com.
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E-Commerce
Ransomware doesnt knock on the front door. It sneaks in quietly, and by the time you notice, the damage is already done. Backups, replication, and cloud storage help recover from ransomware, but when it strikes, these products may not be enough. You copy your data and ensure copies are recoverable when needed. Replication is often viewed as the gold standard of protection. It is fast, efficient, and seems like an easy answer. Two common types of replication are in use today. The first is physical to physical. This is when data is copied from one physical device to another, usually at a remote location. The second is physical to virtual. This is when data is copied from a local physical device to a virtual device in the cloud, commonly managed by a backup vendor. Both replication types can be useful and offer advantages, including uninterrupted service, reduced potential data loss, and data redundancy. But replication has limitations. When ransomware strikes When ransomware hits a server, the infection can spread fast. If replication is active, then corrupted or encrypted data may be copied to the secondary device. Both the original and secondary devices now contain bad data. Instead of serving as a safety net, replication can become a trap locking both environments into a compromised state. Replication can also be complex to set up and maintain, requiring skilled staff. Not every organization has the time, budget, or expertise to set up and maintain a replicated environment. Replicating to a vendors cloud can be expensive. You pay for the storage, and often for recovery and ongoing usage. Plus, if your original server goes down and you need to switch to the secondary server, you still need to rebuild the original serverreinstalling the operating system, reapplying patches, and restoring the previous configuration. This can take time depending on the environment. Where does this leave us? Should we just throw replication out the window? No, replication has its place. It can solve certain problems, especially when the risk of downtime outweighs the maintenance costs. But replication is not a cure-all. It should not be viewed as the primary recovery tool, especially against ransomware. Ask if you’re prepared Some questions can help you determine if you are ready for a cyberattack. Replication is a great tool, but ransomware can often expose its weaknesses: Have you thought about what would happen if ransomed data spread across your replicated systems? Do you know how long it would take to rebuild an original device if you had to switch over? Have you tested your recovery process end-to-end, not just the replication part? Do you understand the true cost of your replication service, including the hidden recovery fees? Look beyond replication Replication is valuable, but it shouldnt be the primary mechanism for recovery from a cyberattack. Replication comes with costs and complexity, and doesnt replace the need for a recovery strategy. So consider replication a tool in the toolbox, not the entire strategy. You need a way to quickly restore an infected device to a clean statewithout worrying whether the compromised data has spread across your replicated environment. Or whether the recovery will cost more than the attack. Users sometimes download files locally or store critical data outside of the replicated environment. A complete recovery strategy must include both servers and workstations to ensure quick recovery, regardless of which devices become compromised. When considering ransomware recovery, explore solutions that provide resilience and data integrity, and enable fast recovery when your data is compromised. Instant recovery is achievable with solutions designed to recover from ransomware and other cyber threats.Elisha Riedlinger is the COO at NeuShield.
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E-Commerce
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