Artificial Intelligence – the hottest word in the global tech market which has made life easier in this busy timeline. AI models are providing autonomous systems, cyber security, automation, RPA, and many other benefits to many industries around the world. Technology and data-centric companies need to know about the upcoming artificial intelligence trends or […]
Artificial Intelligence – the hottest word in the global tech market which has made life easier in this busy timeline. AI models are providing autonomous systems, cyber security, automation, RPA, and many other benefits to many industries around the world. Technology and data-centric companies need to know about the upcoming artificial intelligence trends or AI trends to smoothly boost productivity and efficiency.
Following AI predictions can help in generating customer engagement and driving adoption efficiently and effectively using AI models.
Let us explore some of the top artificial intelligence trends in 2023 to make gains in the highly competitive tech market.
Focus on AI make use of cases with high ROI
Return on investment is always an important element for technology purchases. But with companies looking for new ways to reduce costs and gain a competitive advantage, AI projects are expected to become more common. A few years ago, AI was often seen as investigational, but, according to research from IBM, today 35% of companies report using AI in their business, and an additional 42% report that they are using AI. Searching. Edge AI use cases, in particular, can help increase efficiency and reduce costs, making them an attractive place to focus new investments.
For example, supermarkets and big box stores are investing heavily in AI on self-checkout machines to reduce losses from theft and human error. With solutions that detect errors with 98% accuracy, companies can quickly see a return on investment in just a few months. There is also an immediate return to AI industrial inspection, helping to augment human inspectors on factory lines. Restarting with synthetic data, AI can detect defects at a much higher rate and address a wide variety of defects that simply cannot be captured manually, resulting in more products with fewer false negatives or positive detections.
Generative AI in the Art and Creative Space
Attracting and retaining the mindshare of your customer base is a challenge most enterprises constantly grapple with. To improve your brand recall, you need to consistently generate quality content that is relevant and engaging, and appropriately appropriated for circulation across a wide variety of outlets. Here comes Generative AI, which provides new capabilities to enhance content creation.
Using Generative AI, enterprises can create different types of content such as images, videos, and written content, and reduce turnaround time. Generative AI networks employ transfer-style learning or general adversarial networks to create immersive content from a variety of sources.
Apart from the obvious use cases in marketing, it could potentially revolutionize the media industry. Filming in high definition and restoring old movies, enhanced capabilities for special effects and the creation of avatars in the metaverse are just some of the limitless applications.
Here, larger language models like GPT-3 will again come in handy for generating engaging content across fiction, non-fiction, and academic articles. On many publicly available websites, it is already possible to generate quality images of abstract ideas that are provided by simply written prompts from the user. In areas such as audio synthesis, it is possible to create speech and sounds in thousands of tones and frequencies.
One of the malicious applications that may arise that we need to be vigilant about is the creation of deep fakes (artificially generated fake images and videos), which lead to emerging threats such as spreading fake news and furthering harmful propaganda. Techno Thus, Generative AI will be a major transformative force enhancing our innate creativity in various business activities.
AI in Autonomous Vehicles
AI will play a very important role in all independent vehicles including cars, boats, and planes in the coming year. Baidu plans to set up the world’s largest fully driverless ride-hailing service sector in 2023.
UK-based start-up dRISK plans to build a tool for training autonomous vehicles. It uses AI and multiple data sets such as definition segmentation data, object segmentation data, depth maps, training images, and videos. The start-up enables self-driving cars to quickly identify high-risk edge cases and better spot edge conditions. It upgrades the autonomous capability and safety of driverless vehicles.
German start-up Arcticturn is also offering an AI-based Driver Monitoring System (DMS). It arranges deep learning algorithms to continuously analyze the driver’s eye, face, and head movements. It protects the vehicle and its passengers from accidents caused by human error while ensuring the safety of other vehicles on the road. The productivity of AI already presents in vehicles such as Tesla will be greatly improved and the potential for full automation is also highly anticipated.
Tesla’s competitors including Waymo, Apple, GM, and Ford are introducing choices thus increasing the choice for the customer thereby reducing the dependence on public transport.
Growth in human and machine collaboration
Often seen as a distant use case of edge AI, the use of intelligent machines and independent robots is on the rise. From automated delivery facilities to robots meeting the demands of same-day delivery, to robots monitoring grocery stores to working alongside humans on the production line, these intelligent machines are becoming more common.
For this future to happen, one area that needs to be focused on in 2023 is aiding human and machine collaboration. Automated processes benefit from the power and repetitive tasks performed by robots, leaving humans to perform specialized and dexterous tasks that are more suited to our skills. Expect organizations to invest more in this human-machine collaboration in 2023 as a way to mitigate labor shortages and supply chain issues.
IT Focus on Cyber Security at the Edge
Cyber attacks grew by 50% in 2021 and haven’t slowed down since making it the top focus for IT organizations. Edge computing, especially when combined with AI use cases, can increase the cyber security risk for many organizations by creating a wider attack surface outside the traditional data center and its firewalls.
Edge AI in industries such as manufacturing, energy, and transportation requires IT teams to expand their security footprint in environments traditionally managed by operational technology teams. Operational technology teams typically focus on operational efficiency, relying on air-gapped systems without network connectivity to the outside world. Edge AI use cases will begin to break through these restrictions, requiring IT to enable cloud connectivity while maintaining strict security standards.
In 2023, it is expected to see AI being applied to cyber security. Log data generated from IoT networks can now be fed through intelligent security models that can flag suspicious behavior and inform security teams to take action.
ChatGPT – A chatbot that answers questions and writes essays – Open AI
ChatGPT was launched as a prototype on November 30, 2022, and quickly attracted attention for its detailed responses and clear answers across multiple fields of knowledge. Its uneven factual accuracy was identified as a significant flaw.
The technology was developed by San Francisco-based OpenAI, a research company led by Sam Altman and backed by Microsoft, LinkedIn co-founder Reid Hoffman, and Khosla Ventures. ChatGPT automatically generates text based on written prompts in a fashion that is much more advanced and creative than the chatbots of Silicon Valley’s past.
In a year that has hit headlines for the technology sector with massive layoffs, doomed stock prices, and crypto mayhem, ChatGPT has served as a reminder that innovation is still happening.
In addition, OpenAI continues to collect data from ChatGPT users that can be used to further train and fine-tune ChatGPT. Users are allowed to upvote or downvote responses they receive from ChatGPT; When upvoting or downvoting, they can also fill in a text field with additional feedback.