Artificial intelligence grew by leaps and bounds over the years, leaving its footprint across different sectors, including marketing, healthcare, telecommunication, human resource, government, banking and what have you. The big companies are always on the lookout for new ways to upgrade their workflows. To that end, companies like Apple, Microsoft, Google and Facebook have embraced AI with open arms. Unlimited resources, budget, and market position allow big companies to drive innovations at warp speed. In contrast, small companies find AI beyond their paygrade.

The National Security Commission on Artificial Intelligence (NSCAI) recently published the Final Report for 2021 outlining an integrated national strategy to empower the US in the era of AI-accelerated competition and conflict. NSCAI worked with technologists, national security professionals, business executives and academic leaders to put out the report. According to the report, the US government is a long way from being "AI-ready." Based on the findings, the commission has proposed a set of policy recommendations. The US leads in almost all AI parameters than most countries, including India.

There are four major ways to train deep learning networks: supervised, unsupervised, semi-supervised, and reinforcement learning. We'll explain the intuitions behind each of the these methods. Along the way, we'll share terms you'll read in the literature in parentheses and point to more resources for the mathematically inclined. By the way, these categories span both traditional machine learning algorithms and the newer, fancier deep learning algorithms. For the math-inclined, see this Stanford tutorial which covers supervised and unsupervised learning and includes code samples.

There are AI areas focused on different senses, but vision is fundamental along with natural language. Vision attempts to identify and extract symbols from raw visual data and then use those symbols to make decisions, take actions or produce information. These symbols have many forms: they can be labels from a set used for training, captions, text extracted from the image via OCR, colors, and so on. Not all images are created alike: In general, systems that are good at processing attributes for still images are not necessarily as good for processing video, and vice-versa. Sub-domains of computer vision include scene reconstruction, motion/event detection, tracking, object recognition, and image restoration among many others.

Natural Language Processing (NLP) will enable better understanding all around: we'll talk to our computers; our computers will understand us; and we'll have the Star Trek Universal Communicator in our ears translating any language into our native language in real time (and vice versa). Before we get to long, philosophical, and emotional natural conversations with our computers (as in the movie Her, we can build a lot of extremely useful language-enabled applications that help do things like understand whether someone is getting angry on a support call, write better job descriptions, and disambiguating words whose meaning change depending on context (see this Wikipedia page for a fun list of examples including one of my favorite perfectly grammatical sentences: Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo. Scroll down this page to see modern AI services in action figuring out the emotional tilt of a sentence, translating English into Chinese, and more. Let's explore a few of these capabilities by calling real-world APIs, some from the open source community and others from the major public cloud providers such as Google, Microsoft, and IBM. Check the"I'm not a robot" box, and hit analyze.

For all the talk about artificial intelligence (AI) in financial circles at present – it seems everything is "AI-powered" – it turns out there's a lot less genuine AI in place than we might have imagined. The March 2020 Unlocking AI Playbook: FI Edition, a PYMNTS and Brighterion collaboration, explains that while the use of AI solutions by banks and financial institutions (FIs) skyrocketed 70 percent in a single year (2018-2019), less than 10 percent of all banks say they use AI today. "AI's real-world usage may appear limited compared to the fanfare surrounding it today, but our research aims to accurately depict its adoption, so we precisely define AI," the report states. "Systems fitting our definition must have current business applications and be able to work with and learn from dynamic data sets in real time, and these capabilities must be able to associate with specific entities within a system." Fear of implementation cost and complexity are major deterrents to adoption, with 82 percent of banks turning to existing technology, such as a business rule management system (BRMS), to mimic the data insights promised by true AI.

Precisely defining artificial intelligence is tricky. John McCarthy proposed that AI is the simulation of human intelligence by machines for the inaugural summer research project in 1956. Others have defined AI as the study of intelligent agents, human or not, that can perceive their environments and take actions to maximize their chances of achieving some goal. Jerry Kaplan wrestles with the question for an entire chapter in his book Artificial Intelligence: What Everyone Needs To Know before giving up on a succinct definition. Rather than try to define AI precisely, we'll simply differentiate AI's goals and techniques: Some people use Artificial Intelligence and Machine Learning interchangeably.

The American Council for Technology and Industry Advisory Council (ACT-IAC), the premier public-private partnership dedicated to advancing government through the application of information technology, officially announced the release of the "Artificial Intelligence (AI) Playbook for the U.S. Federal Government." It was produced through a collaborative, volunteer effort by a working group of 133 leaders from government and industry plus academia and associations, hosted by the ACT-IAC Emerging Technology Community of Interest (COI). "The AI Playbook is designed to help the United States Federal Government achieve successful outcomes and reduce risk in its understanding and application of AI technologies," said David Wennergren, CEO of ACT-IAC, "and this important work directly supports the President's Management Agenda (PMA), Cross Agency Priority (CAP) Goal 6 – Shifting from Low-Value to High-Value Work." The Playbook also follows the General Service Administration's Office of Government-wide Policy Modernization and Migration Management (M3) framework used for Shared Services. AI has the power to accelerate government services in fields as diverse as medical research and disaster recovery to help save lives and improve quality of service in impactful ways.

Artificial intelligence (AI) is going to change the way we do business and work as communication professionals. In fact, the revolution has already begun. For communication professionals, it promises to take care of all the mundane tactical activities we currently handle, freeing us to focus on demonstrating our value through the more strategic activities that machines cannot--like influencing the C-suite, connecting our organization's audiences and stakeholders and creating meaning in a world fraught with change. But we can't afford to wait any longer. When our organizations seek advice on how best to communicate about AI, we need to be ready to ask the right questions and advise on the right approach. We also need to know what technology is being used and how it will impact on our organizations' stakeholders.

The American Council for Technology and Industry Advisory Council (ACT-IAC), the premier public-private partnership dedicated to advancing government through the application of information technology, officially announced the release of the "Artificial Intelligence (AI) Playbook for the U.S. Federal Government." It was produced through a collaborative, volunteer effort by a working group of 133 leaders from government and industry plus academia and associations, hosted by the ACT-IAC Emerging Technology Community of Interest (COI). "The AI Playbook is designed to help the United States Federal Government achieve successful outcomes and reduce risk in its understanding and application of AI technologies," said David Wennergren, CEO of ACT-IAC, "and this important work directly supports the President's Management Agenda (PMA), Cross Agency Priority (CAP) Goal 6 – Shifting from Low-Value to High-Value Work." The Playbook also follows the General Service Administration's Office of Government-wide Policy Modernization and Migration Management (M3) framework used for Shared Services. AI has the power to accelerate government services in fields as diverse as medical research and disaster recovery to help save lives and improve quality of service in impactful ways.