Predictions for most amazing AI trends in 2021
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| Robot- one of the appliances of Artificial Intelligence. Photo: Bernmard. |
What is Artificial Intelligence (AI)?
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. According to the builtin.com, AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.
Why is artificial intelligence (AI) important?
| Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making. As an example, most humans can figure out how to not lose at tic-tac-toe (noughts and crosses), even though there are 255,168 unique moves, of which 46,080 ends in a draw. Far fewer folks would be considered grand champions of checkers, with more than 500 x 1018, or 500 quintillions, different potential moves. Computers are extremely efficient at calculating these combinations and permutations to arrive at the best decision. AI (and its logical evolution of machine learning) and deep learning are the foundational future of business decision making. |
Top 7 amazing predictions for AI in 2021
Automated governance will become key for controlling AI applications
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| Photo: SECURit. |
Rogue AI is intolerable. In 2021, enterprises will beef up AI governance across their organizations by instituting strong model assurance within their machine learning operational workflows. It is indicated by informationweek.com that this will become an essential capability for ensuring that AI apps carry out their intended functions accurately while steering clear of privacy violations, demographic biases, and other adverse algorithmic outcomes. Vendors of AI governance platforms will extend their ability to deploy and manage a steady stream of assured models all the way to edge devices.
Adversarial attacks will require countermeasures to protect the AI-driven economy: Resilient AI is fundamental.
In 2021, AI professionals will clamour for a consensus methodology for detecting and dealing with adversarial threats to AI apps, such as edge-based computer-vision systems that rely on convolutional neural networks. Many AI developers will adopt the recently released Adversarial ML Threat Matrix, an open, extensible industry framework for classifying the most common adversarial tactics that have been used to disrupt and deceive ML systems. Many machine-learning DevOps vendors will incorporate this standard -- especially access to their GitHub repositories for a curated repository of adversarial attacks -- into their solutions.
Edge-based AI will crunch neural networks down to their essence: Compact AI is essential.
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| Photo: Smarthostingplans. |
In 2021, AI developers will routinely prune all their models’ neural network architectures, hyperparameters, and other features to fit the hardware constraints of edge platforms. Increasingly, AI-model compilers are automating the compression and tuning of AI models for fast, efficient execution across myriad edge endpoints. As the TinyML revolution picks up speed, we’ll see more AI developers use neural architecture search techniques to find the most compact, efficient structure of a neural net for a specific AI inferencing task. Compression of AI algorithms down to their predictive essence will speed the movement of most AI workloads to run on the microcontrollers embedded in edge devices.
Facial recognition will become a dominant AI-based contactless authentication technology: Authenticated AI is imperative.
In 2021, enterprises will implement facial recognition for strong authentication in a growing range of internal and customer-facing applications. By the same token, the business will increasingly avoid using the technology to inference identity, race, gender, and other attributes that might be sensitive from a privacy, bias, or surveillance standpoint. To the extent that businesses incorporate facial recognition in image/video auto-tagging, query-by-image, and other such applications, it will only be after extensive review by legal counsel. The regulatory sensitivity of this technology, and the legal risks, will only grow for the foreseeable future.
Both Waymo and Cruise will debut on the public markets.
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| Photo: Axiois. |
Autonomous vehicle developers like Waymo and Cruise have massive ongoing cash needs. Public market investors are thirsty for IPOs. The 2020 SPAC boom has provided a novel way for less mature businesses to go public. And SPAC investors have shown a voracious appetite for next-generation mobility companies (see, e.g., Nikola, Velodyne, Luminar, Innoviz, Canoo, Fisker, Romeo Systems).
Waymo and Cruise will take advantage of the market environment by going public in 2021. Fully spinning out from their parent companies—Alphabet and General Motors, respectively—will likely unlock significant enterprise value. Of the two, Waymo is more likely to pursue a traditional IPO.
The total number of academic research papers published on federated learning will surge.
Data privacy is becoming an increasingly urgent issue for consumers and regulators. Given this, privacy-preserving AI methods will continue to gain momentum as the most sustainable way to build machine learning models. The most prominent of these methods is federated learning.
The number of academic research papers published on federated learning has grown from 254 in 2018, to 1,340 in 2019, to 3,940 in 2020, according to Google Scholar. This exponential growth will continue: in 2021, over 10,000 research papers will be published on the topic of federated learning, as listed in Forbes.
The U.S. federal government will make AI a true policy priority for the first time.
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| Photo: Forbes. |
The United States has lagged other countries, notably China when it comes to proactive public policy support for artificial intelligence. This will begin to change in 2021 with a Biden White House and a more motivated Congress.
The Biden administration will put forth, and Congress will pass, a federal budget that dramatically increases government funding for AI. Congress will also pass into law a national strategy for AI that addresses topics like AI ethics, research priorities, national security implications and labour automation.
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