The world of language understanding is undergoing a revolution thanks to the development of large language models (LLMs). These models have already been applied to various applications, such as chatbots, language translation, and even content generation. Now, Meta AI Research has released LLaMA -- Large Language Model Meta AI -- a new state-of-the-art language model designed to help researchers advance their work in this subfield of AI. LLaMA is set to revolutionize business communication and automation by transforming customer experience, content creation, and cybersecurity in the enterprise environment. For example, LLaMA can be used to accurately generate customer-facing content such as emails, product descriptions, and help articles. This will enable businesses to create content quickly and accurately without manually writing every single piece.

Email threat is a serious issue for enterprise security. The threat can be in various malicious forms, such as phishing, fraud, blackmail and malvertisement. The traditional anti-spam gateway often maintains a greylist to filter out unexpected emails based on suspicious vocabularies present in the email's subject and contents. However, this type of signature-based approach cannot effectively discover novel and unknown suspicious emails that utilize various evolving malicious payloads. To address the problem, in this paper, we present Holmes, an efficient and lightweight semantic based engine for anomalous email detection. Holmes can convert each email event log into a sentence through word embedding and then identify abnormalities that deviate from a historical baseline based on those translated sentences. We have evaluated the performance of Holmes in a real-world enterprise environment, where around 5,000 emails are sent/received each day. In our experiments, Holmes shows a high capability to detect email threats, especially those that cannot be handled by the enterprise anti-spam gateway. It is also demonstrated through our experiment that Holmes can discover more concealed malicious emails that are immune from several commercial detection tools.

Don't try to put the cart before the horse: realize that efficient data preparation (and thus interoperable standards) and data quality, especially in the enterprise environment, are a basic requirement for all applications of artificial intelligence. The development of competences and experts in the field of artificial intelligence must take place at least parallel to the process of every technological decision, but not at the end of the implementation of an AI strategy. Outsourcing must not be part of this strategy. 'Not to boil the ocean', in other words: small, agile, consecutive pilot projects alone are not enough to develop an AI strategy. Parallel to the pilot phase, a more far-reaching strategy should be developed together with the management to promote cross-departmental, process-independent and data-driven decision-making and activities.

If the Consumer Electronic Show (CES), that took place in Las Vegas earlier this month, is anything to judge by, Artificial Intelligence (AI) and Machine Learning (ML) will be the buzz words for 2019. Reports of "Jaw-dropping' AI technology abound, including: AI enhanced products are coming into our consumer lives at a dizzying pace. Can we expect to see the same flurry of AI and ML solutions coming into hospitals, schools, retail stores, and hotels in 2019? The short answer is yes. While adoption is slow, and we are unlikely to see the sheer number or diversity of AI and ML solutions increase, AI and ML technology is permeating into enterprise environments.

Cybersecurity is a hot topic for organizations across every industry. Securing networks, hardening systems, and protecting data from cyber threats has become more important than ever, as cyber incidents are on the rise. We asked a few of our C-level industry experts what they think we'll see as cybersecurity trends in 2019 – here's what they had to say: If I'm thinking of cybersecurity trends, I'm led to the following: Utilization of data across the enterprise – As data is shared across an organization, it must be secured. One way to better understand data utilization and security is to apply analytics, data science, and predictive machine learning (ML) models. As a new crop of data science graduates move into security-related positions, this will spur industry recognition of how the application of data models can result in better, more effective security.

I recently caught up with Dr. Bhushan Desam, AI global business leader for Lenovo's Data Center Group to discuss how the digital transformation of business isn't truly possible without incorporating machine learning. As a global business leader, Bhushan is focused on developing AI and machine learning business at DCG. On any given day, Bhushan helps manage Lenovo's overall AI strategy, assists in making product portfolio decisions with engineers and product teams, interfaces with R&T teams, supports global sales teams with customer engagement and deepens relationships with HPC customers and partners. He completed his PhD in engineering at the University of Utah, which introduced Bhushan to high performance computing (HPC) and laid the technical groundwork for his role and responsibilities today. After several years working in the field as a researcher and an engineer, Bhushan re-entered academia in 2011 to pursue a joint program in technology management at MIT Sloan School of Management and School of Engineering.

With the wave of personal assistants, such as Siri, Cortana and Google Assistant, and new start-ups leveraging artificial intelligence (AI) and analytics to build personal companions, it's becoming clear we are moving toward a new voice-controlled relationship with technology. As we have already seen in the consumer market, it is all but a given that these voice-activation systems will eventually make it into the enterprise environment, as the potential benefits of these systems could be tremendous in simplifying and automating activities. Here, Craig Walker, director Cloud Services at Alcatel-Lucent Enterprise, explains that, although it may be a long time before we see the full likenesses of "HAL" from "2001: A Space Odyssey", the technology is already here that can improve the ways businesses operate. Think how much easier it would be for a physician to just say "System: update Mary Smith's chart with the following: "Patient experiencing abdominal pain, issue pharmacy order for 200MG of'SuperAntiGas', signed Dr. FeelBetter." Or in a conference room, instead of the struggle to figure out which remote control puts on the projector and the screen, a simple voice request "System: turn on projector, turn on TV and dim lights."

With the wave of personal assistants, such as Siri, Cortana and Google Assistant, and new startups leveraging AI and analytics to build personal companions, it's becoming clear we are moving toward a new voice-controlled relationship with technology. As we have already seen in the consumer market, it is all but a given that these voice-activation systems will eventually make it into the enterprise environment, as the potential benefits of these systems could be tremendous in simplifying and automating activities. Although it may be a long time before we see the full likenesses of "HAL" from "2001: A Space Odyssey", the technology is already here that can improve the ways businesses operate. Think how much easier it would be for a physician to just say "System: update Mary Smith's chart with the following: "Patient experiencing abdominal pain, issue pharmacy order for 200MG of'SuperAntiGas', signed Dr. FeelBetter." Or in a conference room, instead of the struggle to figure out which remote control puts on the projector and the screen, a simple voice request "System: turn on projector, turn on TV and dim lights."

One of the latest trends in the world of technology and engineering is "machine learning" -- in fact, all of the big technology companies today have invested in artificial intelligence and machine learning projects. The term "machine learning" was first defined by Arthur Samuel, way back in 1959. He defined it as "the ability to learn without being explicitly programmed," which basically means that a machine could learn from its own mistakes and reprogram itself to improve its performance over time. The idea gained popularity in the 90s when the concept of data mining came into existence. Data mining uses algorithms to look for patterns in a given set of information, which led to data-driven predictions and decision making.

One of the latest trends in the world of technology and engineering is "machine learning" -- in fact, all of the big technology companies today have invested in artificial intelligence and machine learning projects. The term "machine learning" was first defined by Arthur Samuel, way back in 1959. He defined it as "the ability to learn without being explicitly programmed," which basically means that a machine could learn from its own mistakes and reprogram itself to improve its performance over time. The idea gained popularity in the 90s when the concept of data mining came into existence. Data mining uses algorithms to look for patterns in a given set of information, which led to data-driven predictions and decision making.