If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
According to Gartner, AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decision-making, and take action. In essence, the concept of AI centres on enabling computer systems to think and act in a more'human' way, by learning from and responding to the vast amounts of information they're able to use. AI is already transforming our everyday lives. From the AI features on our smartphones such as built-in smart assistants, to the AI-curated content and recommendations on our social media feeds and streaming services. As the name suggests, machine learning is based on the idea that systems can learn from data to automate and improve how things are done – by using advanced algorithms (a set of rules or instructions) to analyse data, identify patterns and make decisions and recommendations based on what they find.
During the pandemic especially, it's become overwhelming for small- and medium-sized businesses (SMBs) to answer all of their customer service requests. A Freshworks survey found that companies experienced a 71% increase in overall contact volume between February 2020 and January 2021, and expect it to increase further. At the same time, customers -- while empathetic -- have become more demanding. The same poll shows that 68% of customer service managers have seen an increase in customer expectations. What's a company to do? Automation is one route to more manageable customer experience workloads, potentially.
Opptly, an HR Technology leader and direct sourcing innovator for contingent workforce management, permanent recruitment, and total talent acquisition solutions, announced that it has named global data and analytics leader, Salema Rice to its Advisory Board. Ms. Rice's well-earned reputation as a global leader in the emerging field of AI is only matched by her infectious enthusiasm for creating a better world through all things data. Her extensive industry experience has focused on directing data management strategies, AI transformation, and digital innovation, using AI to effect positive change. She's the Global Managing Director of Applied Intelligence at Accenture and previously worked as Chief Digital, Data & Analytics Officer for Geometric Results (GRI), and as Chief Data Officer at the Allegis Group. She's been named one of the Top 30 Most Inspiring Women in AI, a Top 50 Influencer by Women in Tech, and a Global Data Power Leader by Staffing Industry Analysts (SIA).
Deep North, the intelligent video analytics company, announced the launch of Checkout IQ, its new retail loss prevention solution, which uses computer vision and AI to reduce shrinkage at checkout. The release reflects Deep North's ongoing commitment to empower retailers with data-driven tools to keep their businesses competitive, maximize revenue, and offer great customer experiences. With shrinkage at an all-time high and an increase in organized retail crime, Deep North is providing a new way for retailers to prevent fraud loss and improve their bottom line. Raises $10.5 Million Series A to Help CX Teams Turn Conversations Into Insights and Automation Designed to help retailers reduce retail fraud activities, Checkout IQ works with retailers' existing camera systems. By analyzing camera views, the application identifies items that are being scanned by the customer or the cashier, and this count is cross-referenced with the POS item count to detect any discrepancies.
There's no lack of startups around the world trying to make industrial activities more efficient with artificial intelligence. Some invent robots to assist or replace manual labor, while others use machine learning to help businesses discover insights. Synergies Intelligent Systems falls into the second category. Michael Chang founded Synergies in 2016 in Boston to provide easy-to-use AI-powered analytics tools to medium-sized manufacturers. Having worked at Foxconn in Shenzhen in the late 2000s helping the Apple supplier improve yield rate, or reduce the percentage of defective products, using data analysis, Chang realized that not every factory has the financial prowess to spend tens of thousands of dollars on digitization.
Companies are building software that uses AI to monitor people's behavior and interpret their emotions and body language in real life, virtually and even in the metaverse. But to develop that AI, they need fake data, and startups are stepping in to supply it. Synthetic data companies are providing millions of images, videos and sometimes audio data samples that have been generated for the sole purpose of training or improving AI models that could become part of our everyday lives in controversial forms of AI such as facial recognition, emotion AI and other algorithmic systems used to keep track of people's behavior. While in the past companies building computer vision-based AI often relied on publicly available datasets, now AI developers are looking to customized synthetic data to "address more and more domain-specific problems that have zero data you can actually access," said Ofir Zuk, co-founder and CEO of synthetic data company Datagen. Synthetic data companies including Datagen, Mindtech and Synthesis AI represent a corner of an increasingly compartmentalized AI industry.
"By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated." This is a prediction from Gartner that you will find in almost every single article, deck or press release related to synthetic data. We are repeating this quote here despite its ubiquity because it says a lot about the total addressable market of synthetic data. Let's unpack: First, describing synthetic data that is "synthetically generated" may seem tautologic, but it is also quite clear: We are talking about data that is artificial/fake and created, rather than gathered in the real world. Next, there's the core of the prediction -- that synthetic data will be used in the development of most AI and analytics projects.
FinTech as we know it now is highly specialized and centralized. Blockchain and AI can be catalysts for FinTech 2.0 focusing on holistic solutions with increased transaction speeds, transparency, and security. Furthermore, DeFi may mean a larger pool of investors as more and more people gain access to financial markets. The more investors there are, the more data there will be that would be impossible to process without AI. Blockchain provides the foundation for smart contracts to improve transparency and data management, while AI may be leveraged to scale processes, accelerate transactions, and extract insights from large volumes of data.