Automated machine learning (AutoML)
Improved tools for labeling data and automatic tweaking of neural net designs are two promising areas of automated machine learning, according to Michael Mazur, CEO of AI Clearing, which uses AI to improve construction reporting.
According to Mazur, the demand for labeled data has spawned a labeling industry of human annotators in low-cost countries such as India, Central Eastern Europe, and South America.
The hazards of hiring offshore labor “pushed the market to seek at other strategies to eliminate or minimize this element of the process,” according to the report. Improvements in semi- and self-supervised learning are assisting businesses in reducing the amount of data that must be manually labeled.
AI will become cheaper and innovative solutions will take less time to reach market if the task of selecting and tuning a neural network model is automated.
Gartner forecasts a focus on enhancing the multiple processes required to operationalize these models in the future: PlatformOps, MLOps, and DataOps, according to Gartner. These new capabilities are referred to as XOps by Gartner.
AI-enabled conceptual design
Traditionally, AI has been used to automate data, image, and linguistic analytics procedures.
This is appropriate for well defined repeated duties in the financial, retail, or healthcare industries. However, OpenAI recently released DALLE and CLIP (Contrastive Language-Image Pre-training), two new models that mix language and images to build new visual designs from a written description.
Early research demonstrates how the models may be taught to create innovative designs. An avocado-shaped armchair, for example, was created by providing the AI the caption “avocado armchair.”
Mazur believes that the new models will make it easier to apply AI on a large scale in the creative sectors. “We can expect something similar to disrupt fashion, architecture, and other creative
industries in the not-too-distant future,” Mazur predicted.
Multiple modalities, such as text, vision, speech, and IoT sensor data, are becoming more supported by AI within a single ML model. According to David Talby, founder and CTO of John Snow Labs, an NLP tools vendor, developers are starting to uncover novel ways to integrate modalities to better routine tasks like document understanding.
Visual lab results, genetic sequencing reports, clinical trial forms, and other scanned papers are examples of patient data collected and processed by healthcare institutions. If done correctly, the arrangement and presentation style of this information can aid doctors in better understanding what they’re looking at. AI algorithms that have been trained using multi-modal techniques such as machine vision and optical character recognition may be able to improve the presentation of results and so improve medical diagnosis. To get the most out of multimodal approaches, data scientists with cross-domain expertise like natural language processing and machine vision techniques will need to be hired or trained.
AI-enabled employee experience
IT executives are beginning to voice concerns about AI’s potential to rob or demean employment. According to Howard Brown, founder and CEO of RingDNA, a call center technologies supplier, this is fueling interest in adopting AI to enhance and augment the staff experience. Sales and customer success teams, for example, may find AI support particularly useful in overcrowded departments that are trying to hire people. Howard Brown
AI, when combined with robotic process automation, has the potential to free up sales personnel to engage in more meaningful conversations with customers. It could also be used to improve coaching and training for employees.
Everyone talks about providing a positive client experience, but the best way to accomplish so is to first provide a positive employee experience, according to Brown. IT leaders will need to consider how AI can be used in a way that keeps staff engaged, happy, and productive.
Augmented Processes become increasingly popular
When it comes to innovation and automation in 2022, artificial intelligence and data science will be a part of a larger picture. Data ecosystems are scalable, lean, and supply data to a variety of sources on time. However, a foundation must be established in order to adapt and stimulate innovation. Companies will go a step further in optimising their augmented business and development processes, according to Ana Maloberti, a big data engineer at Globant. Software development processes can be optimized with Artificial Intelligence, and we can expect for a broader collective intelligence and improved collaboration.
Quantum computing has a lot of potential for improving AI and machine learning algorithms. Although the technology is currently out of reach for most people, Microsoft, Amazon, and IBM are beginning to change that by making quantum computing resources and simulators available via cloud models.
“As quantum computers become more powerful and intersect with the increased interest in and experimentation by the ML community, this could set us up for huge breakthroughs in late 2022 and 2023,” said Scott Laliberte, managing director and leader, emerging technology consulting, at Protiviti, a digital transformation consultancy.
Companies could benefit greatly from the convergence of quantum computing with machine learning, allowing them to tackle issues that are currently intractable. Laliberte advises businesses to begin considering the influence of quantum computing on their industry now, and to change their AI plans to allow resources to be allocated to quantum computing and machine learning when the platforms develop
in the next two to three years.
AI ethics and standards come into focus
According to Natalie Cartwright, co-founder and COO of AI banking platform Finn AI, “international partnerships like the Global Partnership on AI have evolved from concepts to realities in 2020.” “By 2021, they will have delivered expertise and alignment on how to leverage AI against major global problems, ensure inclusion and diversity, and stimulate innovation and economic growth.” Algorithm fairness and data transparency are just two of the issues in the spotlight as AI ethics becomes more important to organizations across industries and society as a whole.
Artificial Intelligence will become more explainable
According to Dave Lucas, senior director of product at customer data center Tealium, there will be a greater emphasis on explainability. As more data restrictions are implemented, AI’s trustworthiness will become increasingly important. To effectively grasp and describe how each characteristic contributes to the machine learning model’s final prediction or result.
Voice and Language Driven intelligence
The rise in remote working, particularly in customer service centers, has created a wonderful opportunity to implement NLP or ASR (automatic speech recognition) capabilities. According to ISG’s Butterfield, only about 5% of all client contacts are consistently evaluated for quality feedback. Because one-on-one tutoring isn’t available, businesses can employ artificial intelligence to do routine quality checks on customer knowledge and intent to assure continuing compliance.