Amazon is still seen as a bit of a laggard in the race to develop advanced artificial intelligence, but it has quietly created a lab that is now setting records when it comes to AI performance. Amazon's AGI SF Lab, which is located in San Francisco and dedicated to building artificial general intelligence, or AI that surpasses the capabilities of humans, revealed the first fruits of its work today: A new AI model capable of powering some of the most advanced AI agents available anywhere. The new model, called Amazon Nova Act, outperforms ones from OpenAI and Anthropic on several benchmarks designed to gauge the intelligence and aptitude of AI agents, Amazon says. On the benchmarks GroundUI Web and ScreenSpot, Amazon Nova Act performs better than Claude 3.7 Sonnet and OpenAI Computer Use Agent. A major part of Amazon's plan to compete in the AI market is to focus on building agents, and the new model's abilities reflect its efforts to build a generation of tools that can measure up to the very best available.

This week, OpenAI announced a service that makes it possible for just about anyone to build a custom version of ChatGPT, no coding skills required. The company suggests that users may want to build a bot that knows the rules of all board games, teaches kids about math, or can offer culinary advice. These GPTs, as OpenAI calls them, can also perform simple actions by connecting with internet services, for example searching through emails or ordering products from an online store. You can't fault OpenAI for trying to build on the success of its smash hit ChatGPT. But maybe more chatbots is not what we need?

Traditionally, more complex software is harder to use. Nabeel Hyatt, general partner at Spark Capital, says that if Adept's team (pictured) achieves its full potential, this may no longer be the case. Chatbots rule the day in AI for now, but soon, Adept cofounder David Luan predicts, AI won't just display unsettlingly human responses to typed queries, it will execute them. It will do what you would do with your computer for you. Granted such technology is still years away, but the speed of innovation in the AI space means we're talking about two to three years, according to Luan -- not decades.

In 2016 at TechCrunch Disrupt New York, several of the original developers behind what became Siri unveiled Viv, an AI platform that promised to connect various third-party applications to perform just about any task. The pitch was tantalizing -- but never fully realized. Samsung later acquired Viv, folding a pared-down version of the tech into its Bixby voice assistant. Six years later, a new team claims to have cracked the code to a universal AI assistant -- or at least to have gotten a little bit closer. At a product lab called Adept that emerged from stealth today with $65 million in funding, they are -- in the founders' words -- "build[ing] general intelligence that enables humans and computers to work together creatively to solve problems."

The Ex-Machina Hollywood prophecies are not something we remember with fond feelings. At the rate at which Artificial Intelligence is growing, many have a foreboding of a time in the not-too-distant future when we will be dominated by a cold, emotionless fleet of AI-powered robots. For many people, this has come to mean that AI is the opposite of "human." This article is an attempt to study this phenomenon and to answer the question; "Can artificial Intelligence enhance humanity rather than replace it?" In a bid to do this, let's consider one of the most sacrosanct human traditions; gift-giving.

The Ex-Machina Hollywood prophecies are not something we remember with fond feelings. At the rate at which Artificial Intelligence is growing, many have a foreboding of a time in the not-too-distant future when we will be dominated by a cold, emotionless fleet of AI-powered robots. For many people, this has come to mean that AI is the opposite of "human." This article is an attempt to study this phenomenon and to answer the question; "Can artificial Intelligence enhance humanity rather than replace it?" In a bid to do this, let's consider one of the most sacrosanct human traditions; gift-giving.

In 2015, car-and-rocket man Elon Musk joined with influential startup backer Sam Altman to put artificial intelligence on a new, more open course. They cofounded a research institute called OpenAI to make new AI discoveries and give them away for the common good. Now, the institute's researchers are sufficiently worried by something they built that they won't release it to the public. The AI system that gave its creators pause was designed to learn the patterns of language. It does that very well--scoring better on some reading-comprehension tests than any other automated system.

In 2015, car-and-rocket man Elon Musk joined with influential startup backer Sam Altman to put artificial intelligence on a new, more open course. They cofounded a research institute called OpenAI to make new AI discoveries and give them away for the common good. Now, the institute's researchers are sufficiently worried by something they built that they won't release it to the public. The AI system that gave its creators pause was designed to learn the patterns of language. It does that very well--scoring better on some reading-comprehension tests than any other automated system.

In 2015, car-and-rocket man Elon Musk joined with influential startup backer Sam Altman to put artificial intelligence on a new, more open course. They cofounded a research institute called OpenAI to make new AI discoveries and give them away for the common good. Now, the institute's researchers are sufficiently worried by something they built that they won't release it to the public. The AI system that gave its creators pause was designed to learn the patterns of language. It does that very well--scoring better on some reading-comprehension tests than any other automated system.

We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism. Our attention-RNN language model dynamically increases the scope of attention on the history as the conversation continues, as opposed to standard attention (or alignment) models with a fixed input scope in a sequence-to-sequence model. This allows each generated word to be associated with the most relevant words in its corresponding conversation history. We evaluate the model on two popular dialogue datasets, the open-domain MovieTriples dataset and the closed-domain Ubuntu Troubleshoot dataset, and achieve significant improvements over the state-of-the-art and baselines on several metrics, including complementary diversity-based metrics, human evaluation, and qualitative visualizations. We also show that a vanilla RNN with dynamic attention outperforms more complex memory models (e.g., LSTM and GRU) by allowing for flexible, long-distance memory. We promote further coherence via topic modeling-based reranking.