As we move into 2020, AI and machine learning continue to evolve, influencing businesses, consumer products, and technology at large. Below are key trends and developments to expect over the year, along with challenges and opportunities for organizations adopting these technologies.
1. Shift from Adoption to Automation
- Increased Business Focus: 2020 will see a shift in technology spending from general adoption to automation. Businesses will focus on integrating AI to automate existing processes, particularly in big data management and data quality. AI-driven tools, such as Tamr and Informatica CLAIRE, will help with tasks like data cleansing and detecting outliers or duplicates.
- Revenue Over Velocity: Business value will begin to replace velocity as the most important metric in DevOps. This shift aligns with the growing trend of AI being used not just for innovation but to drive business results and revenue.
2. Faster Computing Power
- AI Algorithm Advancements: We can expect significant progress in AI algorithms, especially in artificial neural networks, as research is still in its early stages. Expect near-daily innovations that push the boundaries of what AI can achieve, including advancements in transparency and interpretability.
- Hardware Innovations: Companies like Intel and Nvidia, as well as newer players like Hailo, are advancing hardware that can accelerate neural network processing. This development will be crucial as industries demand more computing power for real-time machine learning.
3. Mainstream Adoption of Machine Learning by SMBs
- Increased Accessibility: Machine learning will become more accessible to small and medium-sized businesses (SMBs) through easier-to-integrate solutions and tools. Python will continue to be the language of choice for machine learning, lowering the barrier to entry for new users.
- Wider Adoption Across Software Products: Machine learning will be integrated into almost every software category, from ERP to CRM and HR systems. It will become a fundamental component in managing business operations.
4. Consumer-Focused AI and Machine Learning
- Digital Assistants & Smart Devices: AI-powered consumer devices, such as digital assistants (Alexa, Google Assistant), will become even more integrated into daily life, with a growing number of products offering voice-assisted technology.
- AI in Retail: Retailers will experiment with frictionless shopping, where AI helps track products in a customer’s cart and facilitates checkout-free shopping experiences. Although widespread implementation will not happen in 2020, trial deployments will continue.
- Autonomous Vehicles: Fully autonomous driving still faces significant challenges, such as algorithmic errors that can pose safety risks (e.g., incorrect reading of traffic signs). While 2020 will not see full automation, AI-assisted driving will continue to evolve, with AI providing assistance and warnings.
5. Overcoming Barriers in AI and Machine Learning
- Skill Shortages: A major hurdle will be the shortage of skilled machine learning engineers. Companies with access to skilled personnel and large datasets will be best positioned to capitalize on AI’s potential.
- Trust and Ethics Issues: Trust will remain a major barrier, especially regarding autonomous decision-making tools. Concerns over algorithmic bias, where AI systems reflect historical prejudices, must be addressed to ensure fairness and reliability.
- General AI vs. Narrow AI: While 2020 will see advances in narrow AI (where machines excel at specific tasks), general AI—machines capable of learning and applying knowledge across various domains—remains a distant goal.
6. AI in Machine Learning Tools and Platforms
- Automated Machine Learning (AutoML): Platforms like Amazon’s SageMaker and Pachyderm will make it easier for non-experts to create and deploy machine learning models. Automated tools will make the process more efficient and accessible.
- Supervised Learning Leadership: China will continue to lead the world in supervised learning accuracy, while the West will push forward with new methods like active learning, which requires less training data.
Conclusion
2020 promises to be an exciting year for AI and machine learning, with significant strides in automation, faster computing, and broader adoption. However, businesses will need to address challenges such as data privacy, trust, and skill shortages. While we may not see fully autonomous vehicles or widespread AI deployment in every industry just yet, advancements in machine learning will continue to reshape both business operations and consumer experiences.