The arrival of the New Year brings us to think in many areas. What does 2021 hold in store for artificial intelligence? Here are 10 Artificial Intelligence predictions, from academic research to capital markets to regulation. We will take stock in December 2021 to assess the results.
1. Both Waymo and Cruise companies will launch on the public markets.
Autonomous vehicle developers like Waymo and Cruise have ongoing and massive cash flow needs. Public market investors are thirsty for IPOs. The PSPC boom in 2020 provided a new way for less mature companies to go public. And SPAC investors have shown a voracious appetite for next-generation mobility companies (For Example Nikola, Velodyne, Luminar, Innoviz, Canoo, Fisker, Romeo Systems).
Waymo and Cruise will benefit from the market environment by going public in 2021. A complete spin-off of their parent companies, Alphabet and General Motors, is likely to generate significant enterprise value. Waymo is more likely to go through a traditional IPO.
2. A political deepfake will go mainstream in the U.S., fueling widespread confusion and misinformation.
Counterfeit technology is improving and proliferating rapidly. The recent incidents in Gabon and Brazil reflect the destructive potential of this technology in the political sphere. In 2021, dummy content will become widespread in the United States, and a significant portion of the population will believe it to be reliable content. The deepfake will likely be used by featuring politicians having a controversial speech.
In response, some policymakers will step up calls to repeal Section 230 of the Communications Decency Act, arguing that big tech companies should be held accountable for monitoring the spread of counterfeits on their sites. platforms.
3. 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. In this context, privacy-preserving AI methods will continue to gain traction as the most sustainable way to build machine learning models. The most important 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, and then to 3,940 in 2020, according to Google Scholar. This exponential growth will continue: in 2021, more than 10,000 research articles will be published on the theme of federated learning.
4. One of the leading AI chip startups will be acquired by a Major semiconductor company for more than $ 2 billion.
Silicon chips designed specifically for AI workloads are the future of the semiconductor industry. Intel’s $ 2 billion acquisition of Habana Labs last year is a recognition of this reality. In 2021, to avoid being disrupted, another legacy chipmaker will make a major acquisition of an AI chip start-up.
Most likely acquisition targets: Graphcore, Cerebras, SambaNova
Potential buyers: NVIDIA, AMD, Qualcomm, Intel
5. One of the leading AI drug discovery companies will be acquired by a major pharmaceutical company for more than $ 2 billion.
Large pharmaceutical companies have realized that machine learning can revolutionize drug discovery and development. In 2021, a leading pharmaceutical company will pay to acquire an AI drug discovery start-up, bringing its technology and talent in-house.
Most likely acquisition targets: Recursion, Exscientia, insitro, Atomwise
Potential buyers: Bayer, GlaxoSmithKline, Novartis, Bristol Myers Squibb, Eli Lilly, Gilead
6. For the first time, the US federal government will make AI a real political priority.
The United States has lagged behind other countries, including China, in proactive public policy support for artificial intelligence. That will start to change in 2021 with a Joe Biden in power and a more engaged Congress.
The Biden government will propose, and Congress will pass, a federal budget that will dramatically increase public funding for artificial intelligence. Congress will also adopt a national strategy for AI that will address topics such as AI ethics, research priorities, national security implications, and work automation.
7. NLP model with over a trillion parameters will be built.
In 2019, OpenAI released GPT-2, the first NLP model with over one billion parameters (it had 1.5 billion). At the time, that number was considered incredibly high. In 2020, OpenAI released GPT-3, which had 175 billion parameters.
The “arms race” of transformers will continue in 2021 with the publication of the first model with more than 1,000 billion parameters. This model will most likely come from OpenAI and will be named GPT-4. Other organizations that could cross the trillions of metrics milestones include Microsoft, NVIDIA, Facebook, and Google.
8. The “MLOps” category will begin to experience significant market consolidation.
A wave of start-ups developing tools and infrastructure for machine learning has emerged in recent years. We will see significant consolidation in this category in 2021.
Start-ups that build specialized “point solutions” will be picked up by larger players looking to develop complete, end-to-end model development platforms. The dual acquisition of SigOpt and Cnvrg.io by Intel this year is a real canary in a coal mine.
Likely Acquisition Targets: Alectio, Algorithmia, Arize AI, Arthur AI, Comet, DarwinAI, Fiddler Labs, Gradio, OctoML, Paperspace, Snorkel AI, Truera, Verta, Weights & Biases, et al.
Potential buyers: IBM, Microsoft, Amazon, Databricks, DataRobot, Oracle
9. AI will become an important part of the narrative in regulators’ antitrust efforts against big tech companies.
This year, US and European regulatory authorities formally initiated antitrust proceedings against Amazon, Apple, Facebook, and Google. So far, regulators have not explicitly focused on artificial intelligence, as they have articulated antitrust cases against the tech giants.
In the coming year, expect regulators and commentators to start relying on artificial intelligence more frequently as they expose how and why these companies unfairly stifle competition. The main argument will be that corporate data monopolies give them essential advantages in developing efficient machine learning algorithms.
10. Biology will continue to gain momentum as the hottest, most transformative area to which to apply machine learning.
This is both the least measurable and the most important prediction on this list.
In terms of academic research, seed funding, and media attention, biology will increasingly emerge as the area with the greatest impact and consequence in which to apply AI. DeepMind’s landmark achievement, AlphaFold, last month, the ramifications of which will take years to fully materialize, is just a prelude to what humanity will accomplish by applying computational methods and machine learning to the mysteries. of biology.
Source from Forbes by Rob Toews.