We consider offline reinforcement learning (RL) in $H$-horizon Markov decision processes (MDPs) under the linear $q^\pi$-realizability assumption, where the action-value function of every policy is linear with respect to a given $d$-dimensional feature function. The hope in this setting is that learning a good policy will be possible without requiring a sample size that scales with the number of states in the MDP. Foster et al. [2021] have shown this to be impossible even under $\textit{concentrability}$, a data coverage assumption where a coefficient $C_\text{conc}$ bounds the extent to which the state-action distribution of any policy can veer off the data distribution. However, the data in this previous work was in the form of a sequence of individual transitions. This leaves open the question of whether the negative result mentioned could be overcome if the data was composed of sequences of full trajectories. In this work we answer this question positively by proving that with trajectory data, a dataset of size $\text{poly}(d,H,C_\text{conc})/\epsilon^2$ is sufficient for deriving an $\epsilon$-optimal policy, regardless of the size of the state space. The main tool that makes this result possible is due to Weisz et al. [2023], who demonstrate that linear MDPs can be used to approximate linearly $q^\pi$-realizable MDPs. The connection to trajectory data is that the linear MDP approximation relies on "skipping" over certain states. The associated estimation problems are thus easy when working with trajectory data, while they remain nontrivial when working with individual transitions. The question of computational efficiency under our assumptions remains open.

Well, from a technical point of view, we leverage the power of a global crowd to provide some of the world's biggest companies with the high-quality data they need to power their artificial intelligence. We're instrumental to the progression and development of artificial intelligence and we couldn't be prouder or more inspired to be involved in an industry that is changing the world. We bond over our shared love of software engineering, data science, and strong coffee. We like online gaming, running marathons, and team drinks. We celebrate authenticity and diversity and we're invested in what we do.

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In 2022, we heard the word metaverse echoing across all industries with a sharp focus on artificial intelligence and machine learning. The digital world also became increasingly hostile with an increase in cyberattacks and ransomware, shedding light on the need to upgrade protection measures across the globe. We also witnessed more and more robots in action - whether to kill or to make our lives easier. Here are the top 5 tech trends that defined 2022 for us. After Facebook officially changed its name to "Meta" to reflect its metaverse ambitions, a lot of people had trouble processing the concept of metaverse.

Well, from a technical point of view, we leverage the power of a global crowd to provide some of the world's biggest companies with the high-quality data they need to power their artificial intelligence. We're instrumental to the progression and development of artificial intelligence and we couldn't be prouder or more inspired to be involved in an industry that is changing the world. We bond over our shared love of software engineering, data science, and strong coffee. We like online gaming, running marathons, and team drinks. We celebrate authenticity and diversity and we're invested in what we do.

As AI is integrated into day-to-day lives, justifiable concerns over its fairness, power, and effects on privacy, speech, and autonomy grow. Join this VB Live event for an in-depth look at why ethical AI is essential, and how we can ensure our AI future is a just one. "AI is only biased because humans are biased. And there are lots of different types of bias and studies around that," says Daniela Braga, Founder and CEO of Defined.ai. "All of our human biases are transported into the way we build AI. So how do we work around preventing AI from having bias?"

Machine learning has suddenly grabbed attention of the tech crowd, much credit goes to OpenAI's GPT-3 that can even automate creative writing! Such is the untapped potential of machine learning that is eyeing enterprise's eyeballs and their investments! Machine learning or ML in short has applications in real life so common that we often tend to overlook! From opening your phone by facial recognition to the more complex recommender algorithms that influences your decision what you would watch or shop next, machine learning is making quite a noise for now. ML is defined as making machines learn to initiate human actions, through complex coding initiated in Python, R, C, C#, Java and so on.

I've been thinking a lot lately about how technology has become the cure-all elixir, with its increasingly important, and sometimes problematic, role it plays in our lives. This line of thinking was in large part spurred by The Future of Digital Disruption event we co-hosted last month with Oxford University's Saïd Business School. The event was co-moderated by Professor Andrew Stephen from Oxford's Said Business School and Teradata's Martin Willcox, VP Technology (EMEA). Leaders from Audi, Barclays, Kantar, Sony Music, O2 Czech Republic, Facebook, MMA, WPP, Walmart, Teradata and others, as well as leading faculty and researcher's from Oxford's Said Business School Future of Marketing Initiative shared experiences and insights about some of the most complex issues facing leaders today, with a focus on challenges at the intersection of marketing and technology (e.g., analytics, AI, machine learning) and identifying new ways to achieve business growth enabled by technology. The keynote sessions and panel conversations were engaging and varied to encompass the dense topic of how digital disruption will shape the future.

One of the most-hyped – and imprecisely defined – technology trends of the moment is that of artificial intelligence (AI). In some ways, this situation is far from new. AI, sometime referred to as cognitive computing, has gone through cycles of ambitious promises and operational flops since at least the early 1980s. During this decades' long span, what "AI" means – and how it gets used in the enterprise -- has also shifted and evolved. What's new today: solutions that leverage AI capabilities are actually moving from research labs into commercially successful deployments.

Intelligent behaviors commonly associated with humans but exhibited by machines and applied to tasks like problem-solving, automatically completing forms or parsing medical images to recommend diagnoses. In theory, true AI should be able to think like and interact with other humans seamlessly. An application of AI that uses algorithms to find patterns in data without instruction. Machine learning automates a system's ability to learn, so it can improve from experience without being programmed for each task it completes. A machine learning model is "trained" on relevant examples from diverse data sources.