Hyperparameter tuning almost always means search: fit the model at every value on a grid, score each by cross-validation, and keep the winner. For spline regression that search is unnecessary. The optimal resolution can be solved for in closed form, to the accuracy an exhaustive search reaches, at a fraction of the compute. Three ingredients make this possible: classical approximation theory pins the squared bias to a known power of the resolution G, exactly the Kolmogorov n-width of the smoothness class; the basis dimension is an explicit polynomial in G; and leave-one-out error follows from a single fit via the PRESS identity. Balancing the two known curves gives the minimizer analytically. We extend this calculus to many coordinates by replacing ambient input dimension with interaction order, the number of active low-order components in an ANOVA decomposition, yielding a scaling law in which the optimal resolution and error are power functions of the effective density (sample size per active component), with input dimension absent from the exponent. The law becomes an algorithm. KORE (Kolmogorov-optimal Order-aware Resolution Estimation) fits two pilot resolutions, solves a leverage-calibrated 2x2 system for the bias and noise scales, and evaluates the closed-form plug-in resolution with a tiny leave-one-out certificate: about a dozen fits instead of a full grid sweep, with a consistency guarantee as the sample grows. Across additive and sparse pairwise targets up to 80 input dimensions, KORE matches exhaustive 3-fold cross-validation and the full classical ladder (GCV, Mallows' Cp, AIC, BIC) while fitting roughly 8x fewer models; on 36 real tabular datasets it ranks first among 21 methods in accuracy per unit of compute, ahead of tuned boosters and kernel machines. When complexity lives in low interaction order, solving for the resolution beats searching for it.

Hospitality and human instructiveness are essential in connecting with consumers. Few would rather talk to a robot caller to handle their issue than a genuine human being. Additionally, customers typically dislike waiting for a representative for extended periods. And personnel sorting through enormous volumes of calls might produce a massive workload. Conversational AI can be used in a variety of organizations to attend to the needs of specific customers to lessen this issue and increase efficiency.

Startup Kore.AI Inc. said today it's making its "low code" conversational artificial intelligence tools available on a pay-as-you-go basis, expanding their availability to smaller businesses. Backed by Nvidia Corp., Kore.AI offers tools that can be used by companies to easily build AI chatbots or virtual assistants for customer support. The key thing about its tools is that they're low code, meaning users can build chatbots and AI assistants using a visual dashboard, with minimal coding required. That helps companies that lack expertise in AI to build chatbots quickly that can be embedded into their websites or applications, so they can deal with common customer queries. Kore.ai says its Experience Optimization Platform can be applied to various use cases.

Learn more about what comes next. Kore.ai, a no-code automation platform designed for enterprise applications, today announced that it raised $50 million in a series C round led by Vistara Growth and PNC with participation from Next Equity Partners, Nicola Wealth, and Beedie Capital, along with $20 million in debt from Sterling National Bank. The funds, which bring Kore's total raised to over $100 million to date, will be put toward expanding the company's workforce while developing new product features, according to cofounder and CEO Raj Koneru. In 2015, just 10% of organizations reported that they either already used automation technology or would be doing so in the near future. Fast forward to 2019, and that number rose to 37% -- which means that more than one in three organizations are either using AI or have plans to do so.

Unlike humans, conversational artificial intelligence (AI), most commonly deployed today via chatbots, are "up" 100% of the time. Beyond chatbots, automated voice response systems (as annoying as they may still be) and virtual voice assistants all utilize conversational AI to power human-to-machine dialog. Conversational AI is the technology that allows users to ask queries to a machine and get automated responses. The most notable of these machines are the virtual assistants such as Alexa, Siri, and Google Assistant. At the heart of Conversation AI, is the utilization of Natural Language Processing (NLP).

Raj is building his fifth company, a conversational AI platform and solutions venture. I really like the PaaS strategy. Sramana Mitra: Let's start by introducing our audience to yourself as well as the company. Raj Koneru: I'm the Founder and CEO of Kore.ai. I've been an entrepreneur for about 25 years. Prior to this, I built about five companies, starting with an IT services company. The first two companies went public. The latest company that I built is called Kony, which is an enterprise mobile application platform. Sramana Mitra: We know Kony well. We've covered Kony. Raj Koneru: About six years ago, I decided to build this company called Kore.ai. This is a fast-growing company in the conversational AI space, which helps large enterprises automate their interactions with their customers, employees, and partners. Sramana Mitra: If you could start us off with a bit of an ecosystem map of the conversational AI space, that would be great. It is not a crowded

So you're interested in AI? Then join our online event, TNW2020, where you'll hear how artificial intelligence is transforming industries and businesses. The coronavirus pandemic has clearly accelerated our dependency on technology, online activities, and artificial intelligence. AI is particularly important for businesses as it enables personalized services on a massive scale, and customers are increasingly demanding it. However, not every company has the knowledge or the tools to implement AI, nor do they know what is required from them to become AI-driven. In this post, I will discuss what options these companies have.

The coronavirus pandemic has clearly accelerated our dependency on technology, online activities, and artificial intelligence. AI is particularly important for businesses as it enables personalized services on a massive scale, and customers are increasingly demanding it. However, not every company has the knowledge or the tools to implement AI, nor do they know what is required from them to become AI-driven. In this post, I will discuss what options these companies have. It is important to note that while many of the methods described below assist no-coders, they are also suitable for developers, who can enjoy the extra development speed they bring in.

Join us as we bring #AI #chatbots to the enterprise.

Chatbots drive productivity and save time. A lot depends on how the chatbot is designed. A well thought out conversation design is key to make the bot perform as per expectations. Enterprises have complex requirements such as multilingual capabilities, natural language understanding, machine learning and other concepts that are needed to satisfy bot users. This infographic outlines the important elements of developing an efficient chatbot.