Beyond ChatGPT, Bard, and Bing AI: The rise of Artificial Intelligence and its impact on our daily life
Artificial Intelligence And Machine Learning: News
Microsoft has released its inaugural Responsible AI Transparency Report, a detailed document that underscores the tech giant's commitment to developing and deploying responsible artificial intelligence (AI) systems throughout 2023.
In a landmark move, Microsoft has signed an agreement with Brookfield Asset Management to support the creation of 10.5GW of new renewable energy capacity worldwide.
NVIDIA is advancing its experimental ChatRTX chatbot by integrating a broader array of AI models for RTX GPU users.
A group of artists, including photographer Jingna Zhang, and illustrators Sarah Andersen, Jessica Fink, and Hope Larson, have initiated legal proceedings against Google.
Leading news outlets, including the New York Daily News, Chicago Tribune, Orlando Sentinel, and San Jose Mercury News have initiated a lawsuit against OpenAI and Microsoft.
Musician FKA Twigs and Warner Music Group's CEO, Robert Kyncl, testified before the Senate Judiciary Subcommittee on intellectual property this week.
NVIDIA-backed AI firm, Synthesia, has recently introduced its latest innovation, "Expressive Avatars."
OneAIChat, an innovative startup from India, has introduced a unique multi-modal AI aggregator.
The creators of Life2vec, an advanced death prediction tool developed by American and Danish researchers, have issued a warning about fraudulent imitation apps.
BBC wildlife presenter Liz Bonnin found her face being misused in an online ad campaign for an insect repellent spray.
A recent research by Finder has revealed a gender bias in the depiction of business roles by OpenAI's text-to-image generator.
Apple has reportedly resumed negotiations with OpenAI, the creators of ChatGPT, to incorporate artificial intelligence (AI) capabilities into iOS 18.
The second Global AI Safety Summit, a significant event in the field of artificial intelligence (AI), is set to commence soon.
Meta's automated advertisement platform, Advantage Plus shopping campaigns, is facing criticism for overspending on ad budgets and underdelivering on sales.
Chinese tech firm Stardust has recently launched its latest robot, the Astribot S1. It is backed by artificial intelligence (AI) technology.
Apple is preparing to reveal its latest operating system, iOS 18, at the upcoming WWDC 2024 developer conference, on June 10.
Google is on the brink of launching an innovative artificial intelligence (AI) feature for Google Chat on Android.
Catholic Answers, a leading advocacy group, recently unveiled an AI priest named "Father Justin."
EyeEm, a Germany-based photo-sharing platform recently acquired by Spanish firm Freepik, has updated its user agreement.
Google is utilizing its artificial intelligence (AI) technology to assist individuals in learning English.
Coca-Cola is investing $1.1 billion in a five-year partnership with Microsoft to leverage artificial intelligence (AI) for gaining a competitive edge over rivals.
Apple's upcoming software update for iPhones, iOS 18, is generating significant buzz in the tech world.
Elon Musk's artificial intelligence venture, xAI, is close to securing a massive $6 billion in funding, according to TechCrunch.
Dell has unveiled its premium Alienware x16 R2 gaming laptop in India.
Apple is making significant strides in enabling Artificial Intelligence (AI) to operate locally on its devices.
Ray-Ban Meta smart glasses, known for their photo-taking, video-making, livestreaming, and headphone-replacing features, have now integrated multimodal AI.
Xaira Therapeutics, a new entrant in the drug development industry, has successfully raised $1 billion in funding.
Recent studies have underscored the potential privacy risks associated with facial recognition technologies.
Axon, a leading police contractor, and taser manufacturer in the US, has unveiled an innovative product named "Draft One."
Artificial intelligence (AI) is being harnessed to exploit Reddit threads, to promote products, and manipulate Google's search engine rankings through a technique called 'Parasite SEO.'
Bollywood actor, Ranveer Singh is currently at the center of a controversy involving a deepfake video.
Last week, Microsoft researchers introduced WizardLM 2, a powerful open-source language model.
Perplexity, an emerging startup in the artificial intelligence (AI) search field, has raised approximately $63 million in its latest funding round.
Adobe is set to transform its Photoshop software with the introduction of advanced AI tools, supported by the company's state-of-the-art Firefly Image 3 foundation model.
Instagram, a Meta subsidiary, is profiting from advertisements that encourage the creation of nonconsensual nude images through AI-powered apps.
Chinese universities and research institutes have reportedly procured prohibited NVIDIA artificial intelligence chips, despite a US ban extended last year.
Art enthusiasts visiting the Dalí Museum in St. Petersburg, Florida, can now interact with a digital representation of the famed Spanish surrealist artist Salvador Dalí.
The Future of Humanity Institute (FHI), a leading entity in existential risk research related to artificial intelligence (AI), has announced its closure.
Bollywood actor Ranveer Singh has initiated legal proceedings against a deepfake video that falsely depicts him endorsing a political party.
The Pentagon is significantly investing in Big Tech and emerging Silicon Valley companies, specifically targeting artificial intelligence (AI) innovations.
Artificial Intelligence: Cheat Sheet
What is AI in simple terms?In order to help business leaders understand what AI capabilities are, how to use artificial intelligence and where to begin an AI journey, it's essential to first dispel the myths surrounding this huge leap in AI technology.
What is artificial intelligence?AI is largely a pattern-recognition and prediction computer system that can run at a scale dramatically beyond any human, yet never quite replaces humans. Even at its best, AI delivers acceptable — though not perfect — results, letting people step in, observe the data and reason from there.
Note that while we use AI throughout this cheat sheet, most enterprises engage with a subset of AI called machine learning or deep learning. Contemporary usage of the word in business may imply generative AI, in which trained models remix existing content to form images or sentences in new ways. We'll use AI here as a shorthand that includes machine learning and deep learning, and specify generative AI when appropriate.
The truth is that current AI technology is limited, but it's still incredibly powerful. However complicated its processes may seem in practice, at the core of AI-driven applications is the simple ability to identify patterns and make inferences based on those patterns.
AI isn't truly intelligent, and it's often as biased as the data we choose to feed into our ML models. That doesn't mean AI isn't useful for businesses and consumers trying to solve real-world problems — it means that we're nowhere close to machines that can actually make independent decisions or arrive at conclusions without being given the proper data first. It's also true that AI can tend to confirm our biases rather than eliminate them.
How does artificial intelligence work?AI is a complex system designed to model human behavior and intelligence. It combines large amounts of data with algorithms — sometimes modeled after neurons — to analyze, understand and make decisions or predictions about future states. To make accurate predictions, AI systems require large amounts of data to learn from; this data is gathered from various sources, processed, analyzed and organized in a suitable format for the AI algorithms.
AI algorithms are the core of AI systems and are designed to analyze and interpret data, identify patterns and make predictions or decisions based on the input. By continuously collecting new data and retraining the models, AI systems can adapt to changing conditions and improve their performance.
The core process of how AI works involves the following subdomains:
Generative AI systems are capable of creating content, such as images, videos, music or text, that is nearly indistinguishable from human-generated content. They can autonomously generate new outputs based on their learned patterns and styles.
Generative adversarial networks are an example of generative AI, where one network generates content, and another network evaluates and provides feedback to improve the quality of the generated output.
Popular examples of generative AI include:
Artificial intelligence is essentially pattern matching at scale. With its pattern recognition capabilities, modern AI can perform image recognition, understand the natural language and writing patterns of humans, make connections between different types of data, identify abnormalities in patterns, strategize, predict and more.
While humans are unable to as easily comb through the amount of data that machines can to uncover patterns, machines struggle when presented with an outlier that might be easy for a human to spot but contradicts the training data. Therefore, the best AI applications are highly focused and combine human reasoning with the brute power of ML.
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Since the onset of the COVID-19 pandemic in 2020, AI and ML saw a massive market growth. The global pandemic also shifted AI priorities and applications: Instead of solely focusing on financial analysis and consumer insight, post-pandemic AI projects have trended toward customer experience and cost optimization.
AI bots can perform many basic customer service tasks, freeing employees up to only address cases that need human intervention. AI like this has been particularly widespread since the start of the pandemic, when workers forced out of call centers put stress on customer service.
Since then, the release of ChatGPT in November 2022, popularized generative AI.
More must-read AI coverage What are the business applications of AI?In the business world, there are plenty of AI applications, but perhaps none is gaining traction as much as business and predictive analytics and its end goal: prescriptive analytics.
Business analytics is a complicated set of processes that aim to model the present state of a business, predict where it will go if kept on its current trajectory and model potential futures with a given set of changes. Predicting the future with an established model of the past can be easy enough, but prescriptive analysis, which aims to find the best possible outcome by tweaking an organization's current course, can be downright impossible without AI help.
Other AI use cases for businesses include the following:
When adopting an AI strategy, it's important to know what software is available for business-focused AI. There are a wide variety of platforms available from the usual cloud computing suspects like Google, AWS, Microsoft and IBM, and choosing the right one can mean the difference between success and failure.
AWS Machine LearningAWS Machine Learning offers a wide variety of services that run in the AWS cloud. AI services, prebuilt frameworks, analytics tools and more are all available, with many designed to take the legwork out of getting started and others like SageMaker for Business Analysts designed to enable corporations to get AI insight without writing code. AWS offers prebuilt AI algorithms, one-click ML training and training tools for developers getting started in or expanding their knowledge of AI development.
Google CloudGoogle Cloud offers similar AI solutions to AWS, as well as having several prebuilt total AI solutions that organizations can ideally plug into their organizations with minimal effort. Google also distinguishes itself by innovating some of the industry standards for AI like TensorFlow, an open-source ML library.
Microsoft AIMicrosoft's AI platform comes with pre-generated services, ready-to-deploy cloud computing infrastructure and a variety of additional AI tools that can be plugged into existing models. Its AI Lab also offers a wide range of AI apps that developers can tinker with and learn from what others have done. Microsoft also offers an AI school with educational tracks specifically for business applications.
OpenAIDevelopers can choose between a variety of text and image generative AI models from OpenAI, makers of the popular chatbot ChatGPT. As of November 2023, OpenAI offered fine-tuned, custom models based on GPT-4. These custom models are appropriate for organizations with large proprietary datasets and can be accessed through an API. In April 2024, OpenAI added a playground for comparing different models' quality and performance, as well as other quality of life and integration updates.
IBM watsonxIBM's Watson was an early example of question-answering AI and has evolved in several directions as business software. Now, IBM is mostly leaning into watsonx instead, which is a cloud-hosted generative AI and data analytics platform. IBM offers on-site servers custom-built for AI tasks for businesses that don't want to rely on cloud hosting.
What AI skills do businesses need to invest in?Perhaps the most critical skill needed to use AI is knowing when to skip AI altogether. The reality of AI is that many problems could be solved by applying simple regression analysis or if/then statements. Most AI, in other words, isn't AI at all: It's only basic math and common sense.
For more complicated, AI-oriented tasks, the associated data science breaks down into two categories: that which is intended for human consumption and that which is intended for machine consumption.
SEE: Learn what you need to know to become a machine learning engineer.
In the latter case, AI involves complex digital models that apply ML models and AI algorithms to large amounts of data. These systems then act autonomously to generate a particular ad or customer experience or make real-time stock trades. Hence, machine-oriented AI professions require "exceptionally strong mathematical, statistical and computational fluency to build models that can quickly make good predictions," as former Google and Foursquare data scientist Michael Li has noted.
By contrast, the skills needed for more human-oriented data science and AI skew toward storytelling. Given that no data is unbiased, the role of human intelligence is to help the data tell clear stories. Such AI storytellers use data visualization to facilitate exploration and insights into that data.
For many in AI, the most sophisticated math they'll do is power analyses and significance tests. They might write SQL queries to get data, do basic math on that data, graph results and then explain the results. Not gee whiz data science, but it's incredibly helpful for breaking down complex data into actionable insights, to use the data science lingo.
With all that in mind, it's still true that skills needed for an AI project differ based on business needs and the platform being used, though most of the biggest platforms support most, if not all, of the most commonly used AI programming languages and skills needed.
Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.
How can businesses start using AI?Getting started with business AI isn't as easy as simply spending money on an AI platform provider and spinning up some prebuilt models and algorithms. There's a lot that goes into successfully adding AI to an organization.
At the heart of it all is good project planning. Adding artificial intelligence to a business, no matter how it will be used, is like any business transformation initiative. Here's an outline of just one way to approach getting started with business AI.
Determine your AI objectiveTo begin, figure out how AI can be used in your organization and to what end. By focusing on a narrower implementation with a specific goal, you can better allocate resources.
Identify what needs to happen to get thereOnce you know where you want to be, you can figure out where you are and how to make the journey. This could include starting to sort existing data, gathering new data, hiring talent and other preliminary steps.
Build a teamWith an end goal in sight and a plan to get there, it's time to assemble the best team to make it happen. This can include current employees, but don't be afraid to go outside the organization to find the most qualified people. Be sure to allow existing staff to train so they have the opportunity to contribute to the project.
Choose an AI platformSome AI platforms may be better suited to particular projects, but by and large they all offer similar products in order to compete with each other. Let your team give recommendations on which AI platform to choose — they're the experts who will be in the trenches.
Begin implementationWith a goal, team and platform, you're ready to start working in earnest. This won't be quick: AI models need to be trained, testing on subsets of data has to be performed and lots of tweaks will need to be made before a business AI is ready to hit the real world. In fact, you should expect that the vast majority of your time won't be spent crafting trendy algorithms, but rather in data preparation.
SEE: Explore all of TechRepublic's cheat sheets.
Editor's note: This article was updated by TechRepublic's Megan Crouse.
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