Qualcomm’s pivot into the Generative Artificial Intelligence market

Subject :Science and Tech

Section: Awareness in IT & Computers

Context:

Last week, Qualcomm made a series of announcements that highlights the chipmaker’s pivot to Generative Artificial Intelligence (gen AI).
All the products it unveiled were aimed at supporting gen AI applications so mobile units can perform AI based tasks on device.

What is Generative Artificial Intelligence?

GAI is a rapidly growing branch of AI that focuses on generating new content (such as images, audio, text, etc.) based on patterns and rules learned from data.
The rise of GAI can be attributed to the development of advanced generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
These models are trained on large amounts of data and are able to generate new outputs that are similar to the training data. For example, a GAN trained on images of faces can generate new, synthetic images of faces that look realistic.
While GAI is often associated with ChatGPT and deep fakes, the technology was initially used to automate the repetitive processes used in digital image correction and digital audio correction.
Arguably, because machine learning and deep learning are inherently focused on generative processes, they can be considered types of GAI, too.

Applications:

Art and Creativity: It can be used to generate new works of art that are unique and innovative, helping artists and creatives explore new ideas and push the boundaries of traditional art forms.
DeepDream Generator – An open-source platform that uses deep learning algorithms to create surrealistic, dream-like images.
DALL·E2 – This AI model from OpenAI generates new images from text descriptions.
Music: It can help musicians and music producers explore new sounds and styles, leading to more diverse and interesting music.
Amper Music – creates musical tracks from pre-recorded samples.
AIVA – uses AI algorithms to compose original music in various genres and styles.
Computer Graphics: It can generate new 3D models, animations, and special effects, helping movie studios and game developers create more realistic and engaging experiences.
Healthcare: By generating new medical images and simulations, improving the accuracy and efficiency of medical diagnoses and treatments.
Manufacturing and Robotics: It can help optimize manufacturing processes, improving the efficiency and quality of these processes.

Significance for India:

As per NASSCOM data, the overall AI employment in India is estimated at about 416,000 professionals.
The growth rate for the sector is estimated at about 20-25%. Further, AI is expected to contribute an additional USD 957 billion to India’s economy, by 2035.

What are the Concerns Related to GAI?

Accuracy: One of the biggest challenges is ensuring that the outputs generated by GAI are of high quality and accurate.

This requires the development of advanced generative models that can accurately capture the patterns and rules learned from data.

Partisan GAI Models: GAI models are trained on large amounts of data, and if that data is biased, the outputs generated by GAI may also be biased. This can lead to discrimination and reinforce existing societal biases.
Privacy: Training GAI models requires access to large amounts of data, which could include personal and sensitive information.

There is a risk that this data could be used for unethical purposes, such as for targeted advertising or for political manipulation.

Responsibility: Since GAI models can generate new content, such as images, audio, or text it may be used to generate fake news or other malicious content, without knowing who is responsible for the output. This could lead to ethical dilemmas over responsibility.
Automation and Lowering Job: GAI has the potential to automate many processes, which could lead to job displacement for people who are skilled in those areas.