Monday, May 27, 2024

Research Generative AI Applications

Protecting privacy and security in generative AI On device AI and privacy: AI boosts privacy and security

Welcome to AI on the Edge, a new weekly content series featuring the newest device artificial intelligence insights from our most active subject matter experts on this dynamic, ever expanding field.

The rising use of generative AI promises explosive creativity, ease, and productivity. Generative AI is already fulfilling these promises by providing more exact search results, attractive art, personalised advertising campaigns, and new software code using large language models (LLMs) and language-vision models (LVMs).

Must it compromise privacy and security?

Are AI and privacy incompatible?

Not necessarily. On device generative AI lets you enjoy the best of both worlds: AI with privacy and security on your smartphone, PC, or extended reality headset.

When running cloud hosted generative AI models, interactions might be public. The query, context, and data required to fine tune models can be revealed, raising AI and privacy concerns.

This includes private data or source code used as model queries or created by the model for corporate use cases, which is inappropriate.

On device generative AI can improve AI privacy and security.

Why on device? Data privacy and security are improved by AI

On device AI protects user data by keeping inquiries on the device. Under specific conditions, edge devices like smartphones and PCs are trusted to secure sensitive personal and business data using data and communications encryption, password, and biometric access.

Therefore, on device generative AI models may use such security characteristics to increase query and output data security and privacy. Since inference and fine tuning use on device memory, storage, and processing resources, models may use local data to personalise and improve input and output with the same degree of confidence.

Travel ease with on device generative AI

Take this example: User is travelling and seeking for delicious dinners. Devices currently search the Internet and provide local meal alternatives using the user’s location, even with non generative AI. However, with a generative AI based solution, the user may want the chat assistant to use personal data like food and restaurant rating preferences, food allergies, meal plan data, budget, and calendar information to find a nearby four-star restaurant with nutritional options that fit their meal plan.

The user may want the assistant to reserve a table at a time in their schedule after finding an acceptable alternative. In this case, the assistant just uses the cloud to find a list of restaurants and make a reservation while keeping searches and personal information private.

Software developer assistant with on device generative AI

Software developers that need to write product source code benefit from on device generative AI. The generative AI model needs confidential corporate data and code to do this. Again, a coding helper on the developer’s laptop would help protect the company’s valuable intellectual property from cyberattacks.

Retirement planner using on device generative AI

Retirement planning is another broad use of AI privacy. By 2030, all baby boomers in the US will be 65 or older, a population of 73 million.1 Multiple generations of retirees after that have realised the value of a well-funded retirement portfolio. As more individuals reach retirement age worldwide and pensioners live longer, retirement costs rise.

Qualified financial advisers will be in demand as personal portfolio management becomes essential to maximising returns on investment. On device AI might put a retirement planning assistant in an investor’s hand to educate and give the first few tiers of support, streamlining the process once a trained financial adviser is involved. 

Retirement planner
image credit to Qualcomm

The investor might tell the assistant their age, savings, present investments, real estate, income, costs, risk tolerance, and investment goals via a conversational interface. After reviewing this information, the assistant may ask questions to adjust input parameters.

With these factors, the assistant might give educational content, investing techniques, recommended funds, and other investment vehicles. The assistant may also give conversational and graphical scenario analysis based on investor inquiries like “What if she live into my 90s?” or “she just got a new job, how does this affect my current plan?”

The assistant may then utilise the investor’s location, investment level, and risk tolerance to recommend local financial specialists to help develop and implement these first ideas.

Consumer confidence in generative AI requires security and privacy.

All of these instances show how a user would not want a cloud hosted chatbot to access such sensitive information but would be happy with an on device generative AI model to make judgements based on local information. Running generative AI models on a device lets users benefit without revealing personal or confidential information.

Users may want both the results and the prompts that start inquiries protected. Thus, on device inference lets consumers employ AI without exposing their data to cloud hosted models.

Running generative AI models on a device uses current technology protections to use on device personal and business data without the security and privacy issues of cloud hosted models. On device generative AI delivers enhanced creativity, convenience, and productivity and improves on cloud based models.

Up next

How can the industry allow on device generative AI? AI on the Edge will investigate the elements that will increase on device generative AI adoption in future blog entries.

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