🎲 5 data that explain the state of AI in 2024 | Part 2/3
AI data and trends for business leaders | AI systems series
The status of AI in 2024
Please check some facts in last week's edition:
5 more below
Takeaway
1. The United States leads in foundation models
The US is certainly a frontrunner in foundation models, but it's not the only player. Here's a breakdown of the landscape:
Leaders: The US, China, and Europe are all at the forefront of foundation model research, with companies like Google, OpenAI, and IBM leading the charge.
Funding and resources: The US has a significant advantage in terms of research funding and the resources required to train these massive models.
Competition is fierce: China is making big strides in AI research, and Europe also has strong contenders. The competition is pushing the boundaries of what's possible.
Overall, it's a dynamic field with multiple countries vying for leadership.
Explore more on How dependent is China on US artificial intelligence technology? on Reuters
2. Industry calls new PhDs
Industries are actively seeking new PhD graduates:
Deep expertise: PhDs possess in-depth knowledge and research skills in a specific field, which is valuable for tackling complex industry problems.
Research and development (R&D): Many industries heavily invest in R&D, and PhDs can drive innovation by developing new technologies and processes.
Problem-solving skills: Through their research experience, PhDs have honed strong analytical and problem-solving skills, which are highly sought after across industries.
Adaptability: PhD programs train graduates to be adaptable and learn new things quickly, making them valuable assets in a fast-paced business environment.
How industries recruit new PhDs:
Job postings: Companies advertise positions specifically seeking PhD graduates on job boards and company websites.
PhD career fairs: Universities and organizations often host career fairs specifically for PhD students and recent graduates.
Industrial PhD programs: Some research projects are co-funded by universities and companies, allowing PhD students to gain industry experience while completing their dissertations.
Networking: Attending industry conferences and events allows PhDs to connect with potential employers.
Additional resources:
"PhD in industry" programs: These programs combine academic research with industry experience. You can find them by searching for "[Your Country] PhD in Industry Program".
University career centers: Many universities have career centers that offer resources and guidance for PhD students seeking industry jobs.
A PhD can be a great stepping stone for a successful career in industry. Keep in mind that the specific ways industries recruit Ph. D.s will vary depending on the field and the company.
Explore more on the AI Index
3. Some progress on diversity
There has been some progress on diversity in AI, but it's an ongoing effort. Here's a breakdown of some positive developments:
Increased awareness: There's a growing recognition of the importance of diversity in AI development, fueled by research highlighting the dangers of bias in AI systems.
Focus on inclusion: Organizations are making more effort to attract and retain a diverse workforce in AI fields, including women, underrepresented minorities, and people with disabilities.
Educational initiatives: Programs like AI4ALL (AI for All) work to increase diversity in AI by providing educational resources and promoting inclusivity.
Industry efforts: Some tech companies have set specific goals for increasing diversity within their AI teams.
Here are some lingering challenges:
Workforce demographics: The field of AI still struggles with a lack of diversity, particularly in leadership positions.
Data bias: The data used to train AI models can be biased, leading to biased outcomes. Efforts are underway to develop more representative datasets.
Accessibility issues: Underrepresented groups may not access AI education and opportunities equally.
Overall, there's a positive trend towards more diversity in AI, but continued work is needed to ensure a truly inclusive field that develops fair and responsible AI technologies.
Explore more on How to make AI more diverse on Time
4. Chatter in earnings calls
AI has become a hot topic in earnings calls, with companies across various industries mentioning it. Here's a breakdown of the current chatter:
Generative AI: This is a particularly popular theme. Companies highlight their progress in developing and using generative AI tools for content creation, design, and software development tasks. [Digiday](how generative AI has shown up in earnings chatter again this quarter)
AI investment: Many companies emphasize their investments in AI research and development, showcasing their commitment to staying ahead of the curve.
AI for efficiency: Companies are talking about how AI is being used to streamline operations, improve decision-making, and boost productivity.
Competitive advantage: AI is increasingly seen as a key differentiator, with companies highlighting how their AI capabilities give them a competitive edge.
However, there are also some underlying concerns:
Hype vs. reality: Some analysts worry that the focus on AI might be overhyped, and companies must deliver concrete results from their AI investments.
Explainability and bias: There are ongoing discussions about the need for explainable AI models and mitigating potential biases in AI systems.
Overall, the chatter reflects the excitement and potential of AI but also highlights the need for responsible development and concrete business value.
Explore more on How Walmart, Delta, Chevron and Starbucks are using AI to monitor employee messages
5. Costs go down, revenues go up
The combination of decreasing costs and increasing revenue is a powerful driver for the growth of AI:
Reduced costs:
Automation: AI automates tasks previously done by humans, reducing labor costs. For example, AI-powered chatbots can handle customer service inquiries, freeing up human agents for more complex issues.
Improved Efficiency: AI can optimize processes and identify inefficiencies, leading to cost savings across operations. For instance, AI can analyze data to predict equipment failures and schedule preventive maintenance, minimizing downtime and repair costs.
Data-driven Decisions: AI can analyze vast amounts of data to identify patterns and trends humans might miss. This allows businesses to make better decisions, reducing waste and improving resource allocation.
Increased revenue:
Personalization: AI personalizes user experiences, increasing customer satisfaction and loyalty. For example, AI-powered recommendation engines can suggest products or services relevant to a customer's needs and preferences.
Innovation: AI is used to develop new products and services, opening up new revenue streams. For instance, AI can be used to design and develop new drugs, create personalized learning experiences, or automate financial services.
Improved marketing and sales: AI can be used to target marketing campaigns more effectively and automate sales processes, leading to increased conversion rates and revenue growth.
Overall impact:
The combination of reduced costs and increased revenue creates a compelling case for AI adoption. As AI technology develops and becomes more affordable, we can expect to see even greater benefits for businesses across all industries.
Here are some additional points to consider:
The initial investment in AI can be high. However, the long-term cost savings and revenue gains often outweigh the upfront costs.
The specific benefits of AI will vary depending on the industry and application. It's important to carefully evaluate how AI can be used to address specific business challenges.
The ethical implications of AI need to be carefully considered. Bias in AI systems and the potential for job displacement are important issues that must be addressed.
Explore more on Asset managers face rising costs, sluggish revenues but AI might save the day
Resources
With great power comes great responsibility: The ethics of AI
Continue exploring
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