📌 TOPINDIATOURS Update ai: Salesforce rolls out new Slackbot AI agent as it battle

Salesforce on Tuesday launched an entirely rebuilt version of Slackbot, the company's workplace assistant, transforming it from a simple notification tool into what executives describe as a fully powered AI agent capable of searching enterprise data, drafting documents, and taking action on behalf of employees.

The new Slackbot, now generally available to Business+ and Enterprise+ customers, is Salesforce's most aggressive move yet to position Slack at the center of the emerging "agentic AI" movement — where software agents work alongside humans to complete complex tasks. The launch comes as Salesforce attempts to convince investors that artificial intelligence will bolster its products rather than render them obsolete.

"Slackbot isn't just another copilot or AI assistant," said Parker Harris, Salesforce co-founder and Slack's chief technology officer, in an exclusive interview with Salesforce. "It's the front door to the agentic enterprise, powered by Salesforce."

From tricycle to Porsche: Salesforce rebuilt Slackbot from the ground up

Harris was blunt about what distinguishes the new Slackbot from its predecessor: "The old Slackbot was, you know, a little tricycle, and the new Slackbot is like, you know, a Porsche."

The original Slackbot, which has existed since Slack's early days, performed basic algorithmic tasks — reminding users to add colleagues to documents, suggesting channel archives, and delivering simple notifications. The new version runs on an entirely different architecture built around a large language model and sophisticated search capabilities that can access Salesforce records, Google Drive files, calendar data, and years of Slack conversations.

"It's two different things," Harris explained. "The old Slackbot was algorithmic and fairly simple. The new Slackbot is brand new — it's based around an LLM and a very robust search engine, and connections to third-party search engines, third-party enterprise data."

Salesforce chose to retain the Slackbot brand despite the fundamental technical overhaul. "People know what Slackbot is, and so we wanted to carry that forward," Harris said.

Why Anthropic's Claude powers the new Slackbot — and which AI models could come next

The new Slackbot runs on Claude, Anthropic's large language model, a choice driven partly by compliance requirements. Slack's commercial service operates under FedRAMP Moderate certification to serve U.S. federal government customers, and Harris said Anthropic was "the only provider that could give us a compliant LLM" when Slack began building the new system.

But that exclusivity won't last. "We are, this year, going to support additional providers," Harris said. "We have a great relationship with Google. Gemini is incredible — performance is great, cost is great. So we're going to use Gemini for some things." He added that OpenAI remains a possibility as well.

Harris echoed Salesforce CEO Marc Benioff's view that large language models are becoming commoditized: "You've heard Marc talk about LLMs are commodities, that they're democratized. I call them CPUs."

On the sensitive question of training data, Harris was unequivocal: Salesforce does not train any models on customer data. "Models don't have any sort of security," he explained. "If we trained it on some confidential conversation that you and I have, I don't want Carolyn to know — if I train it into the LLM, there is no way for me to say you get to see the answer, but Carolyn doesn't."

Inside Salesforce's internal experiment: 80,000 employees tested Slackbot with striking results

Salesforce has been testing the new Slackbot internally for months, rolling it out to all 80,000 employees. According to Ryan Gavin, Slack's chief marketing officer, the results have been striking: "It's the fastest adopted product in Salesforce history."

Internal data shows that two-thirds of Salesforce employees have tried the new Slackbot, with 80% of those users continuing to use it regularly. Internal satisfaction rates reached 96% — the highest for any AI feature Slack has shipped. Employees report saving between two and 20 hours per week.

The adoption happened largely organically. "I think it was about five days, and a Canvas was developed by our employees called 'The Most Stealable Slackbot Prompts,'" Gavin said. "People just started adding to it organically. I think it's up to 250-plus prompts that are in this Canvas right now."

Kate Crotty, a principal UX researcher at Salesforce, found that 73% of internal adoption was driven by social sharing rather than top-down mandates. "Everybody is there to help each other learn and communicate hacks," she said.

How Slackbot transforms scattered enterprise data into executive-ready insights

During a product demonstration, Amy Bauer, Slack's product experience designer, showed how Slackbot can synthesize information across multiple sources. In one example, she asked Slackbot to analyze customer feedback from a pilot program, upload an image of a usage dashboard, and have Slackbot correlate the qualitative and quantitative data.

"This is where Slackbot really earns its keep for me," Bauer explained. "What it's doing is not just simply reading the image — it's actually looking at the image and comparing it to the insight it just generated for me."

Slackbot can then query Salesforce to find enterprise accounts with open deals that might be good candidates for early access, creating what Bauer called "a really great justification and plan to move forward." Finally, it can synthesize all that information into a Canvas — Slack's collaborative document format — and find calendar availability among stakeholders to schedule a review meeting.

"Up until this point, we have been working in a one-to-one capacity with Slackbot," Bauer said. "But one of the benefits that I can do now is take this insight and have it generate this into a Canvas, a shared workspace where I can iterate on it, refine it with Slackbot, or share it out with my team."

Rob Seaman, Slack's chief product officer, said the Canvas creation demonstrates where the product is heading: "This is making a tool call internally to Slack Canvas to actually write, effectively, a shared document. But it signals where we're going with Slackbot — we're eventually going to be adding in additional third-party tool calls."

MrBeast's company became a Slackbot guinea pig—and employees say they're saving 90 minutes a day

Among Salesforce's pilot customers is Beast Industries, the parent company of YouTube star MrBeast. Luis Madrigal, the company's chief information officer, joined the launch announcement to describe his experience.

"As somebody who has rolled out enterprise technologies for over two decades now, this was practically one of the easiest," Madrigal …

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🔗 Sumber: venturebeat.com

📌 TOPINDIATOURS Hot ai: 500 billion ops per second: China achieves 4-fold computin

Chinese researchers have created a new computing architecture that boosts processing performance by nearly four times, opening up new opportunities in areas such as embodied intelligence, edge sensing, brain-inspired computing, and communication systems.

Hailing from Peking University, the researchers combined two novel devices to match the frequency conversion into a multi-physics-domain architecture. This process resulted into a versatile system that can perform complex operations, including Fourier transform.

About Fourier Transform

At the core of the breakthrough is Fourier Transform, a fundamental technique that converts complex signals, such as sound and images, into their frequency-domain representations and is widely used across science and engineering.

“This architecture enables different computing paradigms to operate within their optimal physical domains, such as electrical current, charge or light, thereby improving computational efficiency,” said Tao Yaoyu, a researcher at the Institute for Artificial Intelligence of this university.’

Tao said the integrated system combines the strengths of two devices in frequency generation, modulation, and in-memory computing. The approach maintains accuracy while lowering power use and boosts Fourier Transform processing speeds from about 130 billion operations per second to roughly 500 billion, marking a several-fold increase.

The new computing architecture could make future hardware more efficient and speed up its use in areas such as foundational AI models, embodied intelligence, autonomous driving, brain-computer interfaces, and communication systems.

Building on earlier advances

The work builds on a growing body of research aimed at overcoming the limits of traditional digital computing. In recent years, scientists have explored neuromorphic, photonic, and analog computing architectures to accelerate core operations such as Fourier and convolution transforms while reducing energy consumption.

Similar experiments have shown that hardware optimized for specific mathematical functions can deliver large speed gains over conventional processors.

By combining multiple physical computing domains into a single system, the Peking University team’s approach reflects a broader push towards architectures designed to support next-generation AI and robotics more efficiently.

The advance comes as traditional computing architectures struggle to keep pace with the growing demands of AI workloads.

A shift beyond conventional designs

By enabling different computations to run in their most efficient physical domains, the new architecture points the way beyond conventional chip designs, potentially reducing energy bottlenecks while delivering higher performance for AI-driven systems.

The research aligns with a global push toward alternative computing approaches, including neuromorphic, photonic, and in-memory computing systems. Previous studies have shown that hardware tailored for specific mathematical operations, such as Fourier and convolution transforms, can significantly outperform general-purpose processors.

The multi-physics approach demonstrated by the Peking University team adds to this momentum, highlighting how specialized architectures could play a key role in the future of AI hardware.

The breakthrough experiment and its result were published in the journal Nature Electronics.

🔗 Sumber: interestingengineering.com