Four Ways AI And Machine Learning Will Drive Future Innovation And Change
Source – https://www.forbes.com/
CTO & MD at AX Semantics, the SaaS-based, Natural Language Generation Platform that creates any content, in any language, at any scale.
2020 was a year most of us want to forget. The pandemic brought on economic, logistical and technological challenges on a massive global scale, leaving businesses scrambling to adapt. Amidst the upheaval, businesses turned to video conferencing platforms like Zoom and Google Meet to stay connected. Technologies like artificial intelligence (AI) and machine learning (ML) helped augment human efforts to take on everything from health to cybersecurity. Equally, businesses looked toward strategic execution and technology to remain agile among industry shifts and provide a greater return on investments.
Businesses are now focused on what’s next and preparing for an economic surge in the latter half of 2021 once more people globally are vaccinated and the world returns to a more “normal” way of life. Here are four ways AI and ML will continue to shape multiple industries and integrate with other technologies to drive further innovation and change in the year ahead:
1. Increased Commercial Applications For “Federated ML”
“Federated ML” or a “cloud-in-a-pocket” approach will play a more prominent role. The applied principles and techniques employed with federated ML means data doesn’t need to be in the cloud anymore. Today’s devices can store more data than ever before and likely more than a user could ever produce. As a result, AI models that help to improve personalized services no longer need to be centralized on company servers, but can instead exist on the device itself.
Equally, the techniques used by federated ML ensure user data is kept on the device and not on a server, while still providing access to predictive AI modeling. Data isn’t shared in the same way either. The advanced ML models used in federated ML keep data in data owners’ hands, leading to greater privacy. This approach is a new take on data privacy and a growing megatrend. A good example of this is Siri running on your iPhone, but not sending all of your data to Apple’s servers.
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Federated ML and its principles are currently in use, but greater commercial applications in this area are on the horizon. The introduction of Apple’s M1 chip and the industry-leading neural engine, for example, was specifically designed for advanced ML processes. Federated ML will also provide increased use cases within the financial services sector in areas like loan risk prediction, while AI and ML applications will also advance many other industries.
2. Promising AI Applications Within The Health Sector
The onset of Covid-19 was a catalyst for advancing technologies in pharma, medicine and the health sector, including a newly updated focus on nursing, patient care, remote patient monitoring and telehealth.
The integration of AI technology promises use cases in terms of data aggregation, updating patients’ charts and analyzing tests and images to suggest possible diagnoses and more. The application of AI in health, in a supporting role, also frees up physicians’ workloads, allowing them to spend more time with patients and on actual patient care. Japan is already looking at augmenting their doctors with AI to combat their doctor shortage.
AI technology is rapidly expanding into other healthcare areas, including early detection of diseases, treatment and research. The technology will only evolve in the year ahead and play a more prominent role, especially as the world continues to weather the effects of Covid-19.
3. Hyper-Personalization Within E-Commerce
It’s true many industries suffered under the weight of the pandemic. E-commerce, however, ballooned. Amazon saw over 5.2 billion visitors worldwide in June 2020 and even temporarily limited delivery to only essential items given an unprecedented flood of orders. Consumers will demand even more customized experiences in 2021, giving rise to hyper-personalization and greater customer experience within the e-commerce sector.
“Algorithmic e-commerce” — or the smart, systemic digitization of business functions often handled manually — will usher in widespread adoption and utilization of AI and ML by enterprises in the e-commerce sector. For example, AI-powered natural language generation (NLG) content will produce an algorithmic e-commerce experience, where customers receive bespoke online shopping experiences through customized product and category descriptions that turn a product page into a personalized sales pitch. Ultimately, this burgeoning trend will lead to a market shift that delivers more value to consumers — where vendors take a more product-type approach to personalization and customer experience, versus a consulting-product approach, as they’ve done previously.
4. New AI And ML Innovations With NLG
Natural language from phonetics, understanding, processing and generation has seen significant advancements in the last few years. As a result, the combination of AI and ML technology with NLG is rapidly pushing the boundaries of what is possible.
Large and small companies already utilize the technology across multiple sectors and industries. We’ve already seen consumer applications like Google phone calls and enterprise applications like business process automation based on unstructured data (i.e., text to voice). Facebook has also achieved impressive results in semi-supervised and self-supervised learning techniques utilizing AI and NLP.
GPT-3, the brainchild of OpenAI, a San Francisco-based AI lab is the third in a series of autocomplete tools designed by the company that provides a “text-in-text-out” interface that provides automatic text completion. This area has gained commercial traction and we’ll see real-life innovations and advancing use cases in this area rapidly accelerate over the course of this year. Areas like CSS generation utilizing GPT-3 and applications similar to Google Smart Compose will gain ground in 2021.
IDC expects worldwide AI spending to reach $110 billion by 2024. Companies should review products and projects closely to capitalize on what AI and ML have to offer — and then laser focus on those that use existing first-party data instead of those that first require the setup and creation of large, complex big data sets and data crunching.
AI and ML technologies are more than buzzwords or simple predictions: They offer businesses limitless possibilities to evolve use cases to improve productivity, expand their customer base, boost ROI and grow their bottom line.