Keeping up with all of the moving parts of digital marketing can be a task. From SEO to PPC, platforms, tools, and best practices the digital landscape is changing constantly as new technologies, techniques and algorithms become available. We know that what worked the last few years may not work the same way in 2022, and to get the best possible results for your business is to stay on top of these trends. As we enter a new year and tech continues to change rapidly, it's a good time to take the opportunity to dive into the digital marketing trends you will see more of in 2022. Google announced that it would end cookie tracking in early 2022.
On April 21, the EU officially proposed the Artificial Intelligence Act, outlining the ability to monitor, regulate and ban uses of machine learning technology. The goal, according to officials, is to invest in and accelerate the use of AI in the EU, bolstering the economy while also ensuring consistency, addressing global challenges and establishing trust with human users. AI use cases with unacceptable risk will be banned outright. High-risk applications, similarly, pose a high risk to health, safety and fundamental rights, though the debate around the definition of "high risk" has been raging since last year, with more than 300 organizations weighing in. These AI applications are allowed on the market only if certain safeguards are in place, such as human oversight, transparency and traceability.
A recent study has used machine learning analysis techniques to chart the readability, usefulness, length and complexity of more than 50,000 privacy policies on popular websites in a period covering 25 years from 1996 to 2021. The research concludes that the average reader would need to devote 400 hours of'annual reading time' (more than an hour a day) in order to penetrate the growing word counts, obfuscating language and vague language use that characterize the modern privacy policies of some of the most-frequented websites. 'The average policy length has almost doubled in the last ten years, with 2159 words in March 2011 and 4191 words in March 2021, and almost quadrupled since 2000 (1146 words).' The mean word count and sentence count among the corpus studied, over a 25 year period. Though the rate of increase in length spiked when the GDPR and the California Consumer Privacy Act (CCPA) protections came into force, the paper discounts these variations as'small effect sizes' which appear to be insignificant against the broader long-term trend.
Ambient computing is all about A.I. and making decisions without human involvement. Ambient computing is a broad term describing an environment of data, smart devices, A.I. decisions, and human activity that allows computer actions alongside daily life, without direct human interventions or commands. This essentially means that computers quietly handle stuff in the background. Although the phrase was coined in the 1990s, it first became prominent in the mid-2010s. Thought leaders assumed that 2021 would be finally the big year for ambient computing. Ambient computing is currently experiencing its moment as both a growth factor in lots of business decisions and an accelerating force towards smart home branding.
Federated learning is a machine learning technique that trains a model over several dispersed nodes or hosts, as the name suggests. If the model parameters are shared between nodes rather than the raw data, the data can be kept private. Due to privacy concerns, obtaining training data to design and evolve machine learning models is increasingly being questioned, and federated learning can help alleviate some of these issues. The Chinese e-commerce behemoth, Alibaba, has created a federated learning platform that allows machine learning algorithms to be constructed without sharing training data.
Chintan Shah is a senior product manager at NVIDIA, focusing on AI products for intelligent video analytics. Chintan manages an end-to-end toolkit for efficient deep learning training and real-time inference. Previously, he developed hardware IPs for NVIDIA GPUs. Chintan holds a master's degree in electrical engineering from North Carolina State University.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Unstructured data is by its very nature difficult to wrangle. It is one of the hardest sources of data to manage, said Amy Brown, founder and CEO of B2B software-as-a-service (SaaS) startup Authenticx. "AI allows organization of this really messy data source," Brown said. Still, she said, "it takes a commitment and a desire to use that data source."
With the Artificial Intelligence Act (AI Act), we have – again – crossed the Rubicon. The die has been cast, there is no way back. We are setting standards for another industry that until now has been left mostly on its own, that has important social functions, and that is of central importance in the global tech rivalry. The European electorate was and still is quite united in demanding rules for digital players while maintaining easy digital access and a competitiveness for all things digital. With the AI Act and other legislation currently under way in such fields as cybersecurity, data, crypto and chips, the European Union is finalizing what it began with the General Data Privacy Regulation (GDPR), the Digital Services Act (DSA) and the Digital Markets Act (DMA). It will surely not be the last time digital policy is undertaken in Brussels, and updates to these regulations are partly already necessary.
Artificial intelligence as a Service (AIaaS), refers to pre-trained computer learning algorithms, natural language processing (NLP), and robotic process automation (RPA) in the cloud to automate business operations. It is very similar to software-as-a-service (SaaS). AIaaS is a service that allows businesses to access AI models, without the need for advanced AI programming skills. This blog lists the expected growth figures for the AI business, including the deployment model, end-user application, verticals, and geography. Companies will not be able to build their own AI solutions for these small services.