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Data analytics and machine learning can deliver real business value, but too many projects miss their mark. Here are seven mistakes to watch out for, and what to do instead.
There is widespread fear in the securities and finance sectors that using generative AI will force companies to rely on giant cloud companies.
AI agents offer flexibility and autonomy as they plan and complete complex tasks that traditionally require human involvement.
Overspending on AI infrastructure by cloud providers has some forecasting an AI bust, but there are signs that enterprises are starting to put AI to work.
The schoolyard chaos at OpenAI could detract from corporate adoption of AI. Microsoft may be best suited to restore order and trust.
Humans, with our biases, fears, and comfortable habits, always put the brakes on revolutionary technology.
The inherent weaknesses of large language models are reason enough to explore other technologies, such as reinforcement learning or recurrent neural networks.
It’s clear that AI, including generative AI, will be tested in the courts. Cloud and AI architects must practice defensive design and governance to stay out of trouble.
Our unrealistic expectations of genAI are like hoping a two-year-old will calmly act like an adult. Try patiently experimenting with prompts and spending ‘quality time’ with this developing technology.
Cloud and AI are the most important technologies today, and both have outstripped open source licenses. It’s time for a new open source definition.
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