Cognizant rolls out platform to help companies with generative AI use

From codesigning to speeding content development processes, generative AI creates new space for creativity. It can input all forms of “unstructured” data—raw text, images, and video—and output new forms of media, ranging from fully-written scripts to 3-D designs and realistic virtual models for video campaigns. Avanade believes that AI is the next wave of computing, a technology that will influence nearly every area of business.

This way, policymakers — with the help of the tech community — can identify which models are too risky to deploy in certain sectors but pose little to no harm in others. Importantly, AI innovation must specify its own human moderation mechanisms (e.g. generative media platforms should have clear provenance procedures before they are deployed in newsrooms). Third, regulatory agencies and innovators must collaborate to create mechanisms that validate the quality and inclusiveness of datasets or models that go into the general deployment of AI. One example is the approach by Nigeria’s National Information Technology Development Agency (NITDA) in co-developing its National AI Policy with a team of 63 industry experts, having received over 350 applications from its call for volunteers home and abroad. This requires constant reiteration during and after the product development stage.

Key Benefits of Generative AI

Ethical risks – Generative content could produce harmful, biased, or misleading messaging without oversight and governance. Strategic focus – AI tools may ramble or lack a central narrative without an understanding of the strategic goals only humans possess. Originality – AI-generated content risks repetition and lacks true creativity without human direction. AIPRM – A pre-programmed genrative ai ChatGPT prompt engineering tool which can write all kinds of marketing material including SEO content, persona development, social posts, paid advertising etc. Generative AI in the audio domain has led to advancements in speech synthesis, music composition, sound effects, and more. Text-to-speech technologies can create lifelike voices for virtual assistants or audiobooks.

Field engineers will frequently need to query guidelines and data such as P&IDs, technical documentation, OEM manuals and work orders. At Faculty, we helped a leading transport company improve its safety-critical communications using an Automatic Speech Recognition (ASR) model for this kind of auto transcription. The solution achieved a word error rate of less than 1%, compared to a typical 4-5% if done by a human or alternative ML models. For example, have your engineers voice record their progress while performing their tasks.

Amplified Efficiency And Productivity

So, let’s start with the hot topic of the past year, which has undoubtedly been generative AI. From ChatGPT – the fastest-growing application of all time – to more niche and experimental tools focused on creating video, music, or 3D design. Generative AI applies to any application where we use AI (ML) to create something. This could mean personalized products, where AI is used to fill in information in order to create customized or bespoke customer offerings. It can also mean computer code, marketing materials, product designs, responses to customer service requests (chatbots), all the way up to synthetic datasets for use in training other ML models or building digital simulations.

Yakov Livshits

Keeping large amounts of student data in one place introduces data breaches and hacking concerns. However, if this information doesn’t correspond to any real people, a breach won’t be as impactful. Training AI models on AI-generated datasets provides anonymity, protecting students’ privacy.

Feeding the recorded data of your operational teams’ communications into an LLM can help identify their strengths and shortcomings with HSE or operational protocols. For companies operating in the B2B industrial sectors, the impact of generative AI may feel remote at first glance. But ignoring its potential for supercharging your operations would be a significant oversight. Moreover, we delved into the myriad benefits of scaling generative AI, from fostering innovation and personalization to optimizing efficiency and gaining a competitive edge. This guide has shed light on how generative AI can empower your business to leap beyond boundaries and redefine its trajectory in a rapidly evolving business landscape. Through meticulous attention to data integrity and protection, you pave the way for innovation and growth while safeguarding the interests of your business and its stakeholders.

This approach ensures a deep understanding of legal nuances for precise and contextually relevant interactions. Generative AI has quickly become a transformative technology in various industries, such as logistics, customer service, and data management – all crucial facets of the retail sector. Many major retailers are turning their attention to this growing technology to revolutionise their business. However, retailers seeking to implement generative AI should understand its potential successes and its risks and regulation. It is important for the firms to explore Generative AI capabilities by identifying a suitable use case which offers business value and helps understand the technology capabilities better. Since the model outputs are highly data dependent, identifying the right set of data for training, data quality and data security measures needs a closer look.

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At a time when incomes are strained during a cost-of-living crisis, and when public services are still rebounding from a once in a generation pandemic, every regulator needs to make a concerted effort to support the responsible adoption of this technology. We quickly tested out our theories and proved that LLMs could be used to help their service work 20x faster. In our view, generative AI is not just automation—it’s genrative ai about augmentation and acceleration. That means giving fashion professionals and creatives the technological tools to do certain tasks dramatically faster, freeing them up to spend more of their time doing things that only humans can do. While still nascent, generative AI has the potential to help fashion businesses become more productive, get to market faster, and serve customers better.

Our accompanying risk toolkit helps organisations looking to identify and mitigate data protection risks. When I started out, we were using punch cards, I don’t know if you’ve ever seen a punch card to enter survey data. And the research at that time, all the research data was collected on pen and paper questionnaires. And we use tape recorders for qualitative research, there was a lot of manual work involved in a lot of paper waste, and looking back now. And things have changed so much since then, it has been quite a fun wide witnessing this evolution of the Insight industry, and the digitization.