The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Leena AI, an AI-powered conversational platform used by major enterprises such as Nestlé, Coca-Cola, and P&G, has raised $30 million in a series B round of funding led by Bessemer Venture Partners. Founded out of New York in 2018, Leena AI is one of numerous conversational AI platforms that enable companies of all sizes to automate conversations through chatbot-like technology. However, Leena AI is carving a niche for itself by focusing specifically on human resource (HR) teams -- it's basically an automated employee helpdesk. Leena AI CEO and cofounder Adit Jain said that his company is setting out to be a "Siri for employees," emulating shifts elsewhere in the technological spectrum -- it's about replacing the old way of doing things with something more in line with what people are accustomed to in their everyday lives.

A global KPMG survey showed that organizations cannot yet reap all the benefits of data analytics due to data quality issues and a lack of capable resources. In 30 years' time, developments in data analytics itself could solve this issue, making many current professions in the sector obsolete. The impossible will become possible, and this may well lead to an autonomous decision-making process. Data analytics is expected to radically change the way we live and do business in the future. Already today we use the analytics in our technology devices, for many decisions in our lives.

The science of today will be the technology of tomorrow. With the same mindset, great passion, and enthusiasm towards technology I penned and tried to encapsulate the technology that shapes human life. I briefed up the introduction of machine learning, its applications with a bunch of methodologies and kept a full stop with a proper conclusion. Machine learning which is one of the finest technology which was been coined by Arthur Samuel of IBM who had developed a computer program for playing checkers in the 1950s. As the program had a very less amount of memory, Arthur initiated alpha-beta pruning.

In collaborative filtering, it is an important way to make full use of social information to improve the recommendation quality, which has been proved to be effective because user behavior will be affected by her friends. However, existing works leverage the social relationship to aggregate user features from friends' historical behavior sequences in a user-level indirect paradigm. A significant defect of the indirect paradigm is that it ignores the temporal relationships between behavior events across users. In this paper, we propose a novel time-aware sequential recommendation framework called Social Temporal Excitation Networks (STEN), which introduces temporal point processes to model the fine-grained impact of friends' behaviors on the user s dynamic interests in an event-level direct paradigm. Moreover, we propose to decompose the temporal effect in sequential recommendation into social mutual temporal effect and ego temporal effect. Specifically, we employ a social heterogeneous graph embedding layer to refine user representation via structural information. To enhance temporal information propagation, STEN directly extracts the fine-grained temporal mutual influence of friends' behaviors through the mutually exciting temporal network. Besides, the user s dynamic interests are captured through the self-exciting temporal network. Extensive experiments on three real-world datasets show that STEN outperforms state-of-the-art baseline methods. Moreover, STEN provides event-level recommendation explainability, which is also illustrated experimentally.

Many applications such as recommendation systems or sports tournaments involve pairwise comparisons within a collection of $n$ items, the goal being to aggregate the binary outcomes of the comparisons in order to recover the latent strength and/or global ranking of the items. In recent years, this problem has received significant interest from a theoretical perspective with a number of methods being proposed, along with associated statistical guarantees under the assumption of a suitable generative model. While these results typically collect the pairwise comparisons as one comparison graph $G$, however in many applications - such as the outcomes of soccer matches during a tournament - the nature of pairwise outcomes can evolve with time. Theoretical results for such a dynamic setting are relatively limited compared to the aforementioned static setting. We study in this paper an extension of the classic BTL (Bradley-Terry-Luce) model for the static setting to our dynamic setup under the assumption that the probabilities of the pairwise outcomes evolve smoothly over the time domain $[0,1]$. Given a sequence of comparison graphs $(G_{t'})_{t' \in \mathcal{T}}$ on a regular grid $\mathcal{T} \subset [0,1]$, we aim at recovering the latent strengths of the items $w_t \in \mathbb{R}^n$ at any time $t \in [0,1]$. To this end, we adapt the Rank Centrality method - a popular spectral approach for ranking in the static case - by locally averaging the available data on a suitable neighborhood of $t$. When $(G_{t'})_{t' \in \mathcal{T}}$ is a sequence of Erd\"os-Renyi graphs, we provide non-asymptotic $\ell_2$ and $\ell_{\infty}$ error bounds for estimating $w_t^*$ which in particular establishes the consistency of this method in terms of $n$, and the grid size $\lvert\mathcal{T}\rvert$. We also complement our theoretical analysis with experiments on real and synthetic data.

The biggest concerns over AI today are not about dystopian visions of robot overlords controlling humanity. Social media algorithms are one of the most prominent examples. Take YouTube, which over the years has implemented features and recommendation engines geared toward keeping people glued to their screens. As The New York Times reported in 2019, many content creators on the far right learned that they could tweak their content offerings to make them more appealing to the algorithm and drive many users to watch progressively more extreme content. YouTube has taken action in response, including efforts to remove hate speech. An independently published study in 2019 claimed that YouTube's algorithm was doing a good job of discouraging viewers from watching "radicalizing or extremist content."

Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided e.g. in consultancy via the use of recommender systems. We explore the special characteristics of such knowledge-based B2B services and propose a process that allows to incorporate recommender systems into them. We suggest and compare several recommender techniques that allow to incorporate the necessary contextual knowledge (e.g. company demographics). These techniques are evaluated in isolation on a test set of business intelligence consultancy cases. We then identify the respective strengths of the different techniques and propose a new hybridisation strategy to combine these strengths. Our results show that the hybridisation leads to a substantial performance improvement over the individual methods.

Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scenarios where the user interactions are extremely sparse. We consider this problem as an automatic identification about whether the user behaviors are rich enough to capture the interests for prediction, and propose an Auto-Quantized Contrastive Learning (AQCL) loss to regularize the model. Different from previous methods, AQCL explores both the instance-instance and the instance-cluster similarity to robustify the latent representation, and automatically reduces the information loss to the active users due to the quantization. The proposed framework is agnostic to different model architectures and can be trained in an end-to-end fashion. Extensive results show that it consistently improves the current state-of-the-art CTR models.

Robots are sweeping the world, from amazon's Alexa to full functioning human-like androids. The internet seems all buzzed at a promise of a future where humans and robots will happily work together. However, there is a dark side to robots that many people are still unaware of. BINA48 employs a mix of off-the-shelf software and customized artificial intelligence algorithms, using a microphone to hear, voice recognition software, dictation software which allows improvement in the ability to listen and retain information during a conversation. This human look-like robot is one of the most advanced robots on this planet.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.