Investing in Artificial Intelligence
MWest Ventures is excited about the expanding capabilities and growing future of artificial intelligence (AI). We strive to accelerate the development and creative applications of AI to solve the world's multifarious problems by supporting early-stage start-ups in this space.
What is Artificial Intelligence?
AI is becoming increasingly useful and popular today, thanks to advances in algorithms, increasing availability of big data, and improvements in computing capabilities and storage. AI can be defined as "the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to only biologically observable methods". Considering intelligence as an integral part of human ability to achieve goals in the world, advances in AI are important in complementing and augmenting human abilities. By leveraging computing ability and robust datasets. AI provides immense capabilities to solve our ever-changing problems and help achieve our goals.
Narrow AI vs. General AI
At present day, we are surrounded by a type of AI called Narrow AI, or "Weak" AI, where a machine is trained to perform specific tasks. Apple's Siri and Amazon's Alexa utilize this type of AI in their robust applications to assist us in our everyday living, from checking the weather to playing music upon request. Google translate, customer-service chatbots, and image recognition systems that identify people in photos are other widely known uses of narrow AI that we interact with today. While narrow AI performs effectively in real-time, it operates a single task by pulling data within a pre-determined data set and does not have emotion or consciousness.
Artificial general intelligence (AGI), however, will be a level of AI rivaling human intelligence such that the machine will gain self-awareness and an ability to solve problems as well as learn and plan for the future, much like humans. Under the category of General or "Strong" AI, AGI remains theoretical, as there are certain capabilities that AGI would have to master to achieve human-like intelligence. As an example, machines are not yet capable of achieving human-like sensory capabilities about color consistency and the ability to form spatial characteristics of an environment purely from sound. Researchers and thought leaders in this field have contrasting views and varying predictions on the exact imminence of AGI becoming a reality—and whether it may ever become a reality. Beyond AGI, is Artificial Super Intelligence (ASI), which would surpass the intelligence and abilities of humans, a possibility that may seem both extremely valuable and daunting. While both forms of Strong AI are not yet realized at the present day, their perceivable potential to augment productivity and provide predictions to solve emerging world problems that would not be achievable by humans are reasons driving exploration, research, and development.
Machine Learning vs. Deep Learning
Advances in AI aim to mimic the structure and function of the human brain, with regards to learning and processing information. Machine learning and deep learning are both subfields of AI, and deep learning can be considered a subfield of machine learning. Deep learning comprises artificial neural networks consisting of more than three layers, inclusive of the inputs and outputs. While "non-deep" machine learning is dependent on humans to determine the hierarchy of features and provide a more structured data set for a machine to learn, deep learning automates much of the feature extraction process to enable the use of a larger dataset. Deep machine learning can use labeled datasets, but it can also ingest raw data such as text and images and distinguish between different categories of data from one another. As the neural networks get larger and systems are fed with larger volumes of data, performance will also continue to improve. This form of machine learning can be scaled for greater possibilities and efficiencies.
Growth Drivers for AI
The application of AI is widespread and continually growing. The global artificial intelligence market expects to grow from $40 billion in 2020 to $51 billion in 2021, at a compound annual growth rate (CAGR) of 28%.
In 2025, this market estimates to reach $171 billion at a CAGR of 35%. Several drivers contribute to the adoption and widespread utility of AI. For one, AI offers the ability to unlock value from the increasing volumes of data produced by enterprises, governments, and consumers.
AI technologies will be able to detect and analyze patterns in large volumes of data, uncovering opportunities for competitive advantage in a limited amount of time. In parallel, the increasingly reliable and decreased cost of digital infrastructure has witnessed a decline in technical barriers to implementing AI solutions. This is owed to the widespread utility of cloud computing, wireless communication networks, and improved low-cost sensors.
AI tools will be key to analyzing trends in data and allow for autonomous monitoring and control of industrial processes to raise productivity and improve quality. AI is also demonstrating utility in transforming business models and integration in many business functions including sales and marketing, supply chain operations, talent management, and customer interactions.
Growing Investments into AI Across Sectors
Investments are pouring in for AI-based companies across various industry verticals, including automotive, healthcare, retail, finance, and manufacturing. Within the United States, the AI sector has already garnered $38 billion in funding in the first half of 2021, already surpassing the $36 billion raised in 2020.
In the second quarter of 2021, notable events include the $2.5 billion funding round achieved for an autonomous driving technology company, Waymo, a subsidiary of Google parent company Alphabet. Waymo is developing its fifth-generation Waymo driver, which operates by utilizing a combination of lidar, radar, and cameras. It has accumulated 20 million self-driving miles on public roads and over 10 billion miles in simulation. Waymo is working steadily towards their vision of the future, having already launched a limited, non-commercial ride-hailing service in San Francisco and securing a partnership with one of America's largest grocery chains to deliver groceries. The company has also partnered with freight companies such as UPS to transport goods in Texas.
Early in 2021, we also witnessed Microsoft's $19.7 billion acquisition of Nuance, an AI-powered healthcare solutions company, as part of Microsoft's efforts to provide industry-specific cloud offerings to customers and partners in the growing healthcare industry. Nuance alleviates the burden of clinical documentation and empowers physicians to deliver superior patient experiences through conversational AI and cloud-based clinical intelligence. The acquisition doubles Microsoft's total addressable market (TAM) to nearly $500 billion in the healthcare provider space.
Across the US, the most well-funded AI start-ups were California's data analytics company Databricks, with close to $1.9 billion in total disclosed equity funding, followed by Tanium based in Washington, a cybersecurity and systems management company, with $1.17 billion in funding. Indigo Ag, an agricultural technology company based in Massachusetts, received $1.15 billion in funding, and Tempus, a data-driven precision medicine company garnered $1.07 billion, Amongst these unicorn companies include XANT, a sales automation platform company based in the state of Utah, with a $1.7 billion valuation.
The applications of AI are diverse and will continue to expand as more investments drive the implementation and development of AI in businesses.
Trends and Considerations for Society
AI opens up a new realm of possibilities, but that does not mean that AI is not without risks. Concerns of AI adoption causing harm to society are not trivial, with increasing fears over job security, issues of privacy breaches, and threats to security and safety. As we move toward the trajectory of AI implementation in businesses, companies are beginning to understand what the risks are and how to approach mitigating these risks.
Based on a survey of over 1000 company Executives, the top-priority applications of AI for their workplace as of 2021 are:
Managing risk, fraud, and cybersecurity threats.
Improving AI ethics, explainability, and bias detection.
helping employees make better decisions.
These priorities are followed by:
Analyzing scenarios using simulation modeling
Automating routine tasks.
Automating routine tasks is no longer the top AI application in 2021, as higher priorities for AI applications deal with the mitigation of risks and biases, and the integration of AI across organizations for improved, data-driven decision-making by employees.
Historically, technology has not only raised GDP but has also brought on other positive benefits, such as the increase of leisure and improvement of health and longevity. Negative impacts could also arise especially in the short-term, where AI adoption could increase stress in the workplace, inequality, or risk aversion as a result of fears over job security. With this in mind, businesses may need to consider actions along two dimensions to achieve greater success for the business and society in the long term.
The first consideration is the extent to which businesses adopt technologies intending to accelerate innovation-led growth, rather than purely focusing on cost reduction and labor substitution. The second is the extent to which businesses will manage labor transitions that will accompany technology adoption, by raising skill levels and ensuring a fluid labor market. Technology leaders appear to do well when focusing on new products and new markets and valuing human capital as an essential element in their strategies. Having the talent to drive and implement digital transformation will be necessary for successful execution, and thus in-house training programs will be vital for members to develop new skills to match an automated workplace.
The consideration of social impacts is incorporated in welfare economics, a specific branch of economics that includes both GDP and components of well-being including health, leisure, and equality, expressed as monetary terms.
A strategy focused on innovation and careful management of the technology transition may double potential growth in economic welfare, based on best-case scenario projections. Businesses need to understand that a proactive management strategy will not only be of interest to society at large but will also benefit companies financially. Adopting technology social responsibility, the "conscious alignment of short and medium-term business goals with longer-term societal goals", will be an important priority for companies in the drastically evolving era of AI.
References
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Microsoft News Center. Microsoft accelerates industry cloud strategy for healthcare with the acquisition of Nuance. April 12, 2021. https://news.microsoft.com/2021/04/12/microsoft-accelerates-industry-cloud-strategy-for-healthcare-with-the-acquisition-of-nuance/
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