Over the past couple of decades, artificial intelligence (AI) has made significant progress thanks to advancements in computer processing power, the availability of vast amounts of data, and the development of improved algorithms. What began with rudimentary models has now evolved into sophisticated applications across various industries, including healthcare, automotive, and gaming.
Today, AI's global impact is massive, and its influence is projected to increase exponentially in the future. As technology continues to advance, more use cases will emerge, delivering value to leaders, customers, and employees — especially during times when productivity needs to be maximized with limited resources. By 2030, experts predict that AI will contribute nearly $16 trillion to the global economy.
This article will explore the top AI research and development trends that will have the most significant impact on AI's scalability.
1. Creative or Generative AI
Generative AI is a sub-sector of machine learning that can create new data or content using existing datasets. This technology is applied in a wide range of applications and has many use cases. One of the companies leading the way in generative AI is OpenAI, which has developed three highly sought-after products: GPT-3, ChatGPT, and DALL-E.
- GPT-3 is a language prediction model that was developed in 2020. It can "autcomplete" text by studying millions of web pages and scientific papers on the internet. With 175 billion machine learning parameters, GPT-3 is capable of producing human-like written content when provided with context such as topics, descriptions, or introductory sentences. However, its output may contain bias since it comes from previously published content that can also have racial, religious, or gender bias.
- ChatGPT is the bot version of GPT-3 that was introduced in November 2022. It is a large language model that can answer questions and execute commands after being trained using human conversations and internet content created by humans. This AI has "learned" how humans respond when asked questions, making it ideal for use as an office assistant or customer service support. However, it may be necessary to audit ChatGPT's performance to prevent misinformation in customer care settings.
- DALL-E is an OpenAI product that became a popular graphic-creation tool in 2022. Its name is inspired by Salvador Dali and the robot character from the Pixar movie WALL-E. By inputting a description or using text prompts, users can generate multiple versions of an image or create new ones from existing images. DALL-E's "in-painting" and "out-painting" capabilities make it useful for branding and creative marketing sectors. OpenAI has set policies to prevent DALL-E from creating violent, adult, or hate images. However, the tool is still prone to bias. Additionally, as more users adopt DALL-E, the tool could be used to create animated art with human-like images and voices through AI-generated text-to-video platforms.
- Another company that has developed AI-powered creative tools is Midjourney. Their platform, Midjourney Studio, uses machine learning algorithms to generate unique designs for brands, including logos, packaging, and web designs. The platform combines human creativity with machine learning to help brands create custom visuals that reflect their unique identities. By leveraging AI, Midjourney Studio aims to streamline the design process while providing personalized solutions for each client.
2. Automated machine learning (AutoML)
Automated machine learning (AutoML) is a powerful technique that uses a machine learning algorithm to automate selecting, tuning, and optimizing a model for a specific data set. This technology allows software or machines to handle time-consuming and repetitive tasks, freeing up valuable human resources.
Two key benefits of AutoML are improved tools for labeling data and the automatic tuning of neural net architectures. Traditionally, the need for labeled data has created a labor-intensive industry of human annotators based in low-cost countries. However, using offshore labor comes with associated risks, prompting the market to seek ways to avoid or minimize this part of the process. Fortunately, improvements in semi- and self-supervised learning have helped companies reduce the amount of manually labeled data needed.
In addition to data labeling, automating the selection and tuning of a neural network model will make AI and machine learning solutions more affordable and reduce the time it takes for new solutions to reach the market. As this technology advances, experts predict a focus on improving various processes required to operationalize these models, collectively referred to as XOps. These capabilities include PlatformOps, MLOps, and DataOps.
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3. Cobots
AI-human collaboration has taken a significant leap forward and is set to evolve further as it ushers in the era of cobots, or collaborative robots. As companies increasingly deploy machines equipped with AI, they can take over repetitive and physically demanding tasks, freeing up human staff to concentrate on specialized duties. Additionally, AI features can help teams detect and respond quickly to defects or failures, thereby improving safety and reducing repair and injury costs.
Cobots are expected to see widespread use in various industries such as:
- Automotive manufacturing: for car assembly, spray painting, surface polishing, systems checking, and retrofitting or reconstructing car production lines to accommodate electric models. Companies with palletizing and welding activities also plan to adopt more cobots with higher payloads and longer reach.
- Agriculture: for seed planting, fertilizer and pesticide application, trespasser and invasive species tracking, and LED lighting, as well as hydroponics for indoor farms.
- Healthcare and hospitality: for sample collection, hospital supply restocking, surgery, injury recovery, and support for health workers in residential and nursing or care homes for the elderly or disabled.
- Food and beverage: for warehousing and food packaging.
- Electronics: for the quality inspection of phone chips, phone chip processors, and printed circuit boards.
- Emerging technologies: for torque sensors, proximity detection sensors, and end-effectors like vacuum, mechanical, pneumatic, and magnetic grippers.
- Defense: for clearing roads of explosive devices and detecting explosives using sensors.
Companies can leverage these machines to tackle labor shortages and supply chain issues. The healthcare, construction, and defense industries may use VR and AR-based learning for safety and cost-reduction purposes, replacing traditional training methods.
4. Advanced Cybersecurity and Surveillance
According to a McKinsey report, AI can be used by hackers to accelerate their attacks' end-to-end lifecycle from a few weeks to just hours or days. As more industries adopt AI resources, critical civil infrastructure, such as providing homes with electricity and water, may be vulnerable to hacking activities. Meanwhile, smaller and less secure organizations will continue to be at risk.
Due to these new threats, there will be an increase in demand for information security companies. They can use security AI for data handling, including classifying, cataloging, integrating, and conducting quality control. It will also offer valuable vulnerability management by monitoring network traffic and identifying patterns that indicate criminal behavior, as well as threat detection. Predictive AI can forecast which of the thousands of alerts pose the most significant risks, and those can be addressed first.
According to IBM's 2022 report, businesses with cyber risk management structures and policies saved an average of 3 million USD and reduced breach lifecycles by 74 days due to rapid detection and response.
Insurance companies may also adopt new technologies and strategies to assess and manage cyber risks as cyber threats become more prevalent. In response, insurers will probably introduce risk-based pricing and exemption clauses for ransomware and cyberattacks.
5. Digital Twinning
Industries are using digital twins, which are digital replicas of objects or processes in the physical world, to create virtual models for simulations that can predict how a product or system will perform. Through AI, this technology is being made more accessible to companies of all sizes.
One example of digital twin technology is NVIDIA's Omniverse platform, which is helping various companies, including the following noteworthy examples:
BMW
BMW Group uses Omniverse as its virtual factory to integrate data from various design and planning tools to create real-time, photo-realistic simulations in a single setting. The virtual space simulates all of BMW's 31 factories, allowing staff from different sites and time zones to optimize details of production processes without the need for physical travel.
Lowe's
Lowe's Companies, Inc. is using Omniverse to simulate two of its stores, allowing personnel to restock shelves, reconfigure layouts, and optimize the customer experience through 3D heat maps that indicate customer traffic and sales performance.
HEAVY.AI (formerly OmniSci)
Omniverse is also enabling HEAVY.AI's HeavyRF tool to develop the wireless network design plans of its telco clients. By simulating real-world environments, telcos can determine the best location for cellular towers and base stations for their 5G infrastructure, reducing site deployment costs and planning cycles.
Other examples of digital twin technology include the creation of digital twin cities, such as the Shanghai Urban Operations and Management Center's digital clone of the Chinese city. Although access to this technology has been limited to high-value use cases, tech firms like Amazon (through TwinMaker) and Prevu3D are working to make it more affordable to smaller companies.
6. Democratized AI
Advancements in AI technology are reducing the expertise required to develop AI models, making it easier to involve subject matter experts in the AI development process. Democratizing AI can speed up development and improve accuracy by incorporating input from frontline experts who can identify areas where new models can provide the most value and pinpoint problems that need to be addressed.
The trend of democratizing AI will follow a trajectory similar to that of computers and networks, which evolved from being used by only a few experts to becoming widely adopted across enterprises. The challenge will be ensuring that data is accurate and accessible while maintaining appropriate safeguards.
This could drive the adoption of AI outside of existing IT services. However, democratizing AI also presents cost, ethical, and data privacy implications for enterprises. CIOs will need to audit newer uses of AI to consolidate costs, identify risks, and streamline workflows.
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7. AI-as-a-Service (AIaaS)
AI and cloud computing have seen consistent growth over time, leading to the emergence of a new service: AI-as-a-Service (AIaaS). This cloud computing model allows third-party providers to offer platforms for customers to create and implement AI applications.
Cloud-based AI platforms are becoming increasingly popular, as they provide a way for businesses to quickly access the power of AI without investing in their own infrastructure. Google's TensorFlow, Amazon's SageMaker, and Microsoft's AzureML are some of the most popular cloud-based platforms that offer a variety of AI services, including machine learning, natural language processing, and computer vision, that can be easily integrated into different applications.
As we move further into 2023, we can expect to see more businesses adopting cloud-based AI platforms to take advantage of the benefits they offer. However, this may also raise concerns regarding data privacy and security that need to be addressed by the providers.
8. AI for personalization
Personalized AI Applications are becoming increasingly popular across various industries.
In eCommerce, although 62% of consumers have expressed concern about AI bias, the same Salesforce poll shows that 69% of respondents are willing to accept AI usage by brands if it can enhance their shopping experience. With 91% of consumers are already interacting with chatbots, which are mostly AI-powered, the trend of using AI marketing tools to personalize product searches, recommendations, and messages will continue to grow.
In entertainment, AI is increasingly relied upon in filmmaking, where movie companies use audience analysis tools to discover the best stories for their upcoming productions. Screenvision Media introduced its proprietary Cinelytics to advertisers in 2022, while Warner Bros. adopted a similar technology earlier to predict ticket sales. Starting in 2018, 20th Century Fox has co-developed Merlin Video with Google's Advanced Solutions Lab to forecast their audience's interest based on the AI's analysis of movie trailers. Netflix also utilizes its subscribers' watch history to suggest what to watch next.
Despite concerns about AI bias in the workplace, market insiders predict that AI tools will continue to be an option for improving engagement through team communication software such as Glint and Leena.AI, and for workplace learning using platforms like Hone and EdApp.
According to PwC, 54% of executives at companies already using AI see an increase in employee productivity, while 80% believe automation is beneficial in any business decision.
9. No-Code AI Platforms
The accessibility of AI technologies is a key factor in their popularity. The emergence of no-code AI platforms has significantly lowered the barrier for even small companies to adopt cutting-edge AI capabilities. Working with the latest artificial intelligence solutions is no longer a privilege reserved for large corporations that can afford expensive and lengthy software development from scratch.
Here's what makes no-code solutions appealing:
- Rapid development and implementation, taking 90% less time than writing code from scratch, with the appropriate data collection and processing, as well as subsequent debugging.
- Low development costs, thanks to automation, eliminating the need for independent data collection and training.
- Ease of use – software can be created simply by using drag-and-drop functionality, without writing any code yourself.
No-code development is particularly useful in cases where customization of the developed products is not crucial. Companies often use these options for computer programs that identify and classify images, objects, poses, sounds, etc. Google Cloud Auto ML, Google ML Kit, Runaway AI, CreateML, and MakeML are among the most popular such environments. Our guide will help you prepare to use no-code AI platforms for your business.
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Stay ahead of the competition and build your AI-based software project today! With the rapid evolution of AI technology, it's essential for businesses to stay up-to-date on the latest trends to succeed in 2023 and beyond. Investing in a comprehensive AI strategy now will position companies for success and ensure a bright future. At Intersog, we provide top-quality AI software development services to help our clients optimize the use of these AI development trends and stay ahead of the curve.
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