07 Jun 2021

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Dear Reader,

From Amazon Alexa to Siri, Artificial Intelligence (AI) has gained serious footholds in our personal lives. However, when it comes to IT operations platforms, AI and Machine Learning technologies are still in the nascent stages.

Incidentally, the forces of digital business transformation are necessitating a change to traditional IT management techniques. Hence, advanced approach to IT-Ops, also known as AI-Ops (Artificial Intelligence for IT Operations) is being developed, which combines algorithmic and human intelligence to provide full visibility into the state and performance of the IT systems.

AI-Ops bridge three different IT disciplines - service management, performance management and automation - to accomplish its goals of continuous insights and improvements. Gartner predicts that the use of AI-Ops by large enterprises to monitor applications and infrastructure will rise from 5% in 2018 to 30% by 2023.

In this reference, SysTrack has developed AI-Ops-based platform which incorporates natural language processing (NLP) and AI-driven sentiment scoring. The integration of NLP powers an easy-to-use search function that enables the IT Team to gain holistic idea about their environment. Through integrations with AI-Ops platforms, including IBM Operations Analytics – Predictive Insights, the platform provides a detailed real-time and historical endpoint data that IT operations need to execute AIOps use cases, including proactive monitoring. The NLP engine, allows IT to type or speak questions about their environment in plain English and return suggested answers for accelerated root cause analysis.

Multiple financial institutions are also incorporating AI-Ops in their business functions. For instance, US Bank is leveraging AI-Ops to increase automation across the business lines by analyzing large, monitoring-driven data sets. With the help of AI-Ops, US Bank envisions that the query and complaint resolution will be more effective, delivering better up time and improving customer experience.

Similarly, Similarly, Barclays Bank is using AI-Ops platform through AI and machine learning to analyse end-to-end analysis of IT infrastructure based on various monitoring tools to pull together the required information in order to deliver the best user experience.

As machine-learning systems become more accurate and reliable, routine and well-understood actions can be triggered without human intervention, potentially resolving issues before users are impacted or even become aware of any problem.

Credits : Akhil Handa Manish Kulkarni

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AI in Entertainment

With the emergence of various innovative technologies, filmmakers are effectively saving time and money by automating their film creation process to a large extent. Filmmakers are utilizing AI in various stages of filmmaking, right from pre-production, production to the post-production stage.
Cinelytic, Los Angeles based startup, has developed AI software for filmmakers to predict the probability of a movie's success based on the casting of actors/actresses. If the filmmaker is unsure of which actor to cast for a role, he can enter their names into the program and see which actor might
Belgium based ScriptBook, has developed an AI cloud-based software Script2Screen to analyse the script and predict box office success or failure. The user can upload their screenplay on the platform which generates an AI-based assessment indicating the commercial and critical success of a project, along with insights on the storyline, target demographics, market positioning, distribution parameters etc.
Vault ML, an Israeli startup, launched an artificial intelligence platform 4CAST. It uses over 40 years of box office data, 4 lac plus movie scripts and numerous other film data to predict opening weekend box office sales. Its predictions are right 75 % of the time and thus it helps filmmakers in their business risk management.
On the other hand, 20th Century Fox has launched the AI/ML program Merlin to help review trailers before they are released. The AI program optimises the marketing and distribution efforts in order to generate wider publicity for the specific movie or web series. It predicts and recommends appealing clips and flags them as the parts of the trailer which the audience will enjoy the most.
Similarly, Microsoft has developed an AI speech translation engine Azure for dubbing Hindi movies into different languages. On this platform, Bollywood movies are dubbed with the help of AI in different languages.
Looking at the fact that there is a plethora of data available in the film ecosystem, many companies are coming up with innovative solutions where these softwares read the historical data about movie performances over the years, then cross-references it with the current information and thus helps in writing scripts, simplifying the production process, casting actors, composing music, editing movies, plan marketing and distribution and calculate the return of investment.
Credits : Akhil Handa

AI-powered Legal Tech.

From how we buy our groceries to how we conduct banking transactions, almost every aspect of our lives is being disrupted with continuous advancements in technology. Even traditional, not-so-tech-savvy industries like legal services are joining the tech bandwagon. As per research by CB Insights, legal tech startups have raised over $ 700 mn in funding since 2011. Leveraging emerging technologies like AI & ML, variety of tech-enabled start-ups are devising new and interesting ways to connect consumers to the legal ecosystem.
Generally, it has been observed that the full gamut of AI’s capabilities are best utilized when digitized documents are available. With the Supreme Court digitizing a whopping 10 million pages and records of civil appeals this year, AI is increasingly being adopted by numerous law firms. For example, AI-based products can be applied in prioritisation of pending cases, summarising court proceedings and improving the quality of research.
Some firms like NearLaw have developed legal market places using AI and Natural Language Processing (NLP) to help lawyers and legal professionals access legal information. It has also developed AI-enabled case-law research tools driven by summarisation algorithms coupled with machine learning to rank the cases using Case Ranking. Such tools enable lawyers to know which cases are better suited to be cited in the courts over others and also provide analytics on how the network of cases are inter-related.
While other firms are leveraging AI to offer virtual legal research assistants like CaseIQ, which automate mundane tasks, reducing research time from anywhere between 5% to 50% of what is normally needed on legacy system. Even Judges can upload both the appellant’s counsel’s submission and the respondent’s counsel’s submission directly into CaseIQ and within seconds see whether both parties are missing out on any important precedents that are important to the case, enabling them to quickly take into account the whole body of applicable law before ruling.
Similarly, SpotDraft has developed an artificial intelligence-powered platform that uses advanced machine learning algorithms to automate drafting and negotiating legal contracts. It provides solutions to complex legal dealings, including drafting, managing and storing paperwork online, analysing contracts and automating invoices, besides sending reminders and offering expert advice on legal compliances. Even legal firms such as Cyril Amarchand Mangaldas are now leveraging the power of AI for contract analysis and review by adopting Canada-based machine learning legal system Kira.
Although AI can prove to be useful in predicting what documents will be relevant to a case, we feel that AI will not replace lawyers but will augment their abilities. It has the potential to make them more productive, efficient and aware about their domain of work.
Credits : Akhil Handa

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