Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation.
Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings.
AI pioneers like Google and Amazon, who have adopted these new technologies to create a growing competitive advantage, have already witnessed bottom-line benefits from their AI strategies.
While enterprise AI adoption is still in the early stages, the scale of the opportunity in AI demands more C-level discussions within any business. It is crucial then to have a concrete understanding of AI, its ecosystem, and how industry leaders are taking steps to drive unfair advantage from it.
Let’s take a look at the 13 AI trends that are having the biggest impact in 2019.
The future of labor-intensive work on assembly lines in factories is the replacement of people with AI-programmed robots. This would bring down the cost of labor, reduce outsourcing, and eliminate offshoring.
Georgia-based start-up “SoftWear Automation” empowers manufactures to automate the production of apparel with sewing robots.
In Japan, by 2025, more than 80% of eldercare will be performed by robots, not human caregivers.
Machine learning, a crucial component of AI, refers to the training of algorithms using large data sets to identify desired patterns.
U.K.’s IntelligentX introduced the world’s first AI brewed beer.
DeepFish in Russia is using neural networks to identify fish.
Sweden’s Hoofstep is raising venture capital to develop deep learning-based behavioral analysis for horses.
China is set outpace the U.S. and other western countries in the realm of AI. The Chinese government is making significant investments in AI technologies.
The Chinese government is promoting an “intelligence plan” that includes everything from smart agriculture and intelligent logistics to military applications. China has also poured billions into face recognition technology.
In 2017, Chinese artificial intelligence startups absorbed 48% of all dollars going into AI startups globally, eclipsing the U.S.
China now publishes six times more deep learning patents than the United States.
In July 2017, the Chinese government plans to reach the level of the U.S. in AI technology innovation. China has the goal of becoming the world leader in AI by 2030.
A Chinese government-backed project involves the creation of a GPU with 20 times the performance and energy efficiency of NVIDIA’s GPUs.
Chinese company Cambricon has pledged to produce over one billion AI processing units over the next three years and is developing microprocessors specifically designed for deep learning
The battleground of the future will rely on smart technology. With the increasing convergence of conventional defense, surveillance, and reconnaissance with cybersecurity, the need for algorithm-based AI expands exponentially.
Cyber security is an area of significant opportunity for AI because cyber-attacks are constantly-evolving and becoming more sophisticated.
Prima facie, builds AI software that sifts through millions of cyber security incidents to identify aberrations, risks, and signals of future threats.
The cyber defense market is mushrooming with the majority of new cyber security companies leveraging machine learning.
A total of 134 startups have raised $3.65B in equity funding in the last 5 years. About 34 of them raised equity for the first-time last year. These new startups are competing with market leaders such as Cybereason, CrowdStrike, Cylance, and Tanium.
Voice-enabled computing had a massive impact on the 2019 Consumer Electronics Show (CES). IoT devices are now consistently integrating AI voice assistants.
The market leaders are Amazon Echo and Google Home. Samsung is working on its own voice assistant called Bixby. Samsung wants all of its products to be internet-connected with intelligence from Bixby by 2020.
Skilled professionals, including lawyers, consultants, financial advisors are now being replaced by AI.
Artificial intelligence has the potential of improving efficiency in legal work. As AI legal platforms become more efficient, affordable and commercialized, there will be a downward pressure on the hourly rates law firms charge.
Artificial Intelligence is no longer limited to powerful supercomputers; it is becoming integrated into smartphones and wearable devices. Edge computing is emerging as a major element in the evolution of AI.
Apple released its A11 chip with a neural engine for iPhone 8 and iPhone X. Apple claims the A11 chip can perform machine learning tasks with up-to 600 billion operations per second.
Dedicated processing power is now being built into mobile devices using super-efficient mobile processors optimized for AI operations. For example, AI is now being used to train your personal AI assistant locally on your device to recognize your unique accent or face.
Neural networks use a myriad of architectures. One of the most popular neural network architectures today is convolutional neural networks (CNNs). A new neural network technology called “capsule networks” is beginning to supplant convolutional neural networks
Convolutional neural networks have limitations that impede performance and introduce security issues.
Capsule networks allow AIs to identify general patterns with less training data and are less susceptible to false results. Capsule networks can take relative positions and orientations of an object into consideration without needing to be trained exhaustively.
In 2019, the approximate number of qualified researchers currently in the field of AI is 300,000; including students in relevant research areas. However, companies require a million or more AI specialists to meet their engineering needs.
In the U.S., a Glassdoor search for “artificial intelligence” returns 32,000 active job openings with salary ranges well into the 6 digits. Companies are more than willing to pay top-dollar for AI experts.
As tech giants like Google, Amazon, Salesforce, and Microsoft improve their enterprise AI capability, it will be become very difficult for smaller players to sustain growth.
Google released Cloud AutoML that empowers their customers to bring their own data and train algorithms to suit their specific needs.
Amazon began selling AI-as-a-Service with “Amazon AI” under their Amazon Web Services (AWS) banner.
AI is revolutionizing medical science. Regulators in the U.S. are on the cusp of approving AI for use in clinical settings. The advantage of AI in diagnostics is higher early detection accuracy.
Machine learning algorithms can compare a medical image with millions of other patients, picking up on nuances that the human eye may otherwise miss.
Consumer-focused AI monitoring tools like SkinVision use computer vision to monitor suspicious skin growths.
Recently, AstraZeneca, a Swedish pharmaceutical and biopharmaceutical company, announced a partnership with Alibaba (subsidiary Ali Health) to develop AI-assisted screening and diagnostics applications in China.
GE and Nvidia have joined forces to bring deep learning capabilities to GE’s medical imaging devices.
Open source software libraries, hundreds of APIs and SDKs, and easy assembly kits from Amazon and Google are lowering the barrier to entry into AI.
Google launched an “AI for all ages” project called AIY (artificial Intelligence yourself). Their first product was a voice recognition kit for Raspberry Pi that enabled the users to give any voice they desired to their personal voice assistant.
AI reached a peak in 2017 and is entering a period of exponential year-over-year growth.
Last year investors poured over $15.2B into funding AI startups across multiple industries. There was a 141% spike in AI funding in 2016.
Since 2017 over 1,100 new AI companies have raised their first round of funding.