What is AI and ML?

What is Artificial Intelligence and Machine Learning, and what have the past 6 months taught us about their presence and influence within the tech sector, and their potential future impact in today’s ever-changing, digital-forward climate?

Reference: Pitchbook

Artificial Intelligence, or AI, is intelligence demonstrated by machines, as opposed to the natural intelligence inhabited by animals and humans. AI utilises deep learning and natural language processing to make it possible for machines to learn from experience and adjust to new inputs in order to mimic the problem-solving and decision-making capabilities of the human mind and perform human-like tasks (such as driving a car, or playing a virtual game of solitaire).

There are three types of AI:

  • Artificial narrow intelligence (ANI), also called “weak” or “narrow” AI, has a limited yet powerful function.
  • Artificial general intelligence (AGI), also called “strong” or “deep” AI, mimics human intelligence and behaviour.
  • Artificial superintelligence (ASI), whose intelligence greatly surpasses a human’s.

Subsets of Artificial Intelligence:

  • Machine learning (ML): A subset of AI that often uses statistical techniques to give machines the ability to “learn” from data without being given explicit instructions for how to do so. This process is known as “training” a “model” using a learning “algorithm” that progressively improves model performance on a specific task.
  • Deep Learning (DL): An area of ML that attempts to mimic the activity in layers of neurons in the brain to learn how to recognise complex patterns in data. The “deep” in deep learning refers to the large number of layers of neurons in contemporary ML models that help to learn rich representations of data to achieve better performance gains.
  • Computer Vision (CV): Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”
  • Natural Language Processing (NLP): Enabling machines to analyse, understand and manipulate human language. NLP helps computers communicate with humans in their own language, making it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important
  • Artificial Neural Networks (ANN’s): An artificial neural network is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner. ANNs are created by programming regular computers to behave as though they are interconnected brain cells.

Where is Artificial Intelligence today?

While AI models have grown exponentially over the past several years, their operations remain limited in scope and scale within organisations, meaning growth has not translated directly to improved commercial gain. What’s more, the current unpredictable economic climate has made way for the evaluation of cloud computing costs, resulting in consideration from companies seeking cost-effective AI solutions. Analytics spending data strongly indicates that efficiency and data acquisition are taking president over the company budget or model accuracy and complexity.

AI’s economic downfall (the first we have seen since 2011) is a result of shifts in the mix of spending between cloud computing, on-premise and edge, as opposed to an overall AI plummet. IT spending remains on track to continue double-digit growth globally this year, and as a result AI market size estimate is largely unchanged from Q1 (with fewer companies devoting large budgets over $500,000 and $5 million to AI initiatives), suggesting that budget growth is coming under control.

Areas of AI and ML that received funding in 2022

Although funding has slowed down in recent times, we continue to see startups receiving investments within the AI and ML space. Some notable key players in this area include Doppel, Elloe AI, Inflection and Tenyx, which were all founded this year. The largest deal amongst this group was for Inflection, coming in at £208.86M.

Venture Capital Activity

In Q2 alone, 1,340 total Venture Capital deals were made within the AI sector, with $20.2B collectively being raised across the board; a -27.8% deal value growth QoQ, compared to 21.6% for IT more generally. High-growth AI spaces include accounting automation, wealth management, metaverse and quantum AI.

Let’s take a look back at some highlights and key contributors across Q2 of 2022:

In April we saw Hugging Face (an open-source NPL model startup) raise a $100.0 million Series C at a $1.9 billion pre-money valuation. Their GitHub star count exceeds that of Meta’s Pytoch AI training framework, indicating its establishment in the AI community, and its GitHub contributor count has accelerated growth throughout 2022.

We also saw SoundHound (a voice recognition unicorn) complete a SPAC merger at a $1.9billion valuation, achieving a 5.9x MOIC.

In May, we saw Snowflake complete its $800.0 million acquisition of Streamlit, facilitating support for Python-based machine learning analytics on top of its cloud data warehouse.

In June, we saw Nvidia partner with MLOps startups Run:AI and Weights & Biases for Machine Learning operations. Both startups respectively focus on hardware acceleration and model development.

We also saw IBM acquire AIOps startup Databand for $150.0 million, granting the startup an 8.1x MOIC.

Finally, we saw Databricks (a leading company in Database management), disclose new ML management and monitoring capabilities entitled MLflow 2.0 at the company’s AI + Data Summit. The company also bolsters support for stream data processing with its Project Lightspeed initiative.

Emerging opportunities

We dived into the emerging areas of AI & ML, and below are areas where there has been a significant investment.

  • Code completion
    AI has struggled to learn coding syntax and outperform human coders yet has reached commercialisation for predictive code completion
    Top Startups: Builder.AI | TabNine
  • Streaming data pipelines
    Database management innovators are focusing on stream processing as part of their AI strategies via M&A and product development
    Top Startups: Databricks | DataStax
  • Synthetic data
    Computer-generated data can produce a market opportunity at the scale of the hand-labeled data market
    Top Startups: Synthesis AI | MOSTLY AI

We can help!

An important thing to check before partnering with a recruitment firm is whether they have proven experience and a cultural fit that matches yours.

Next Ventures is your best choice to achieve your hiring goals in AI/ML. We can help you reach the next level in staff augmentation and scale your business sustainably. Contact us to learn more about how we can help your business thrive.

andrew mcloughlin

Andrew Mcloughlin

Director – Emerging Technology