Putting the fun in fundamentals! A collection of short videos to amuse beginners and experts alike.

Jakaitis has proven success creating and scaling advanced analytics functions in insurance. Most recently, as Director of Business Intelligence for Carrot Fertility, she founded and scaled the company's data function, including overseeing full-lifecycle product management for data products, such as ROI models, engagement and utilization projections, and financial forecasting tools. Prior to that, she served as the Head of Marketing Science at Acrisure Technology Group and was a founding member of Altway Insurance, where she led a cross-functional team in go-to-market and marketing strategy for products in the insurance technology space. She also contributed industry and community thought leadership in the areas of marketing optimization, authenticity in marketing, applied AI, and algorithmic marketing. "Leah's skillsets in these areas are vital to the rapid growth of PCF as we continue to advance our use of and leverage our data and technology to inform strategy to benefit our partners and clients," said Rob Smith, President, Agency Operations.

Going into the new year, the market outlook remains positive on the need for talent, particularly with regards to new talent in digital, marketing and regulatory environments. Clients are keen to engage strategic talent pipelining exercises which at this stage shows their approach to 2019. Women on the boards "We are seeing boards from all sectors tackle this fairly evenly, however Financial Services in particular still has a way to go. Ultimately real investment in carefully architected diversity programme is required and right now is lacking. Some of the strategies are quite short-term and tactically focused – much like in politics, you need long term plans that may take a generation to realise. Some of these short-term strategies can feel reactionary and a box ticking exercise – we have had searches when senior female executives have asked us "have I been approached" because I am a woman. Searches focused on diversity only as opposed to the best-qualified candidate are risky – candidates want to feel they deserve recognition as opposed to qualifying via birth. So in 2019 I would like to see more programmes focus as much on the intake level and be delivered throughout the organisation via talent management programmes at all levels over many years. This will increase the supply of qualified, diverse talent rather than chase a finite pool for the sake of experience. This is a big investment and would need the backing of investors/shareholders, the public and the CEO. In reality, both approaches are required to ensure today's diverse talent is being recognised fairly as well. Talent management The importance, and more importantly, the influence that these functions now have has multiplied over the past ten years and we will see more companies move to have HR representation on their boards in 2019. The functions have grown in quality and capability and can now work hand in glove with external partners to ensure the right results when hiring critical senior leaders. Culture plays a massive role in achieving growth as retention is as critical to growth as recruitment. Opportunity is what attracts talented people – you need opportunity and culture to retain talent. Without retention, growth will be severely impacted. Going into 2019, we will see companies with HR & talent management represented on their boards, create better cultures and therefore realise stronger revenue growth".

Making Friends with Machine Learning was an internal-only Google course specially created to inspire beginners and amuse experts.* It is one of Google's best-loved educational offerings of all time. Curious to know what's in there? The course is designed to give everyone -- no matter your role -- the tools you need for effective participation in machine learning for solving business problems and for being a good citizen in an increasingly AI-fueled world. MFML is perfect for humans of all stripes; it focuses on conceptual understanding (rather than the mathematical and programming details) and guides you through the ideas that form the basis of successful approaches to machine learning. It has something for everyone!

"Digital transformation" is not just a popular buzzword -- it's the standard that many businesses strive toward. But what does "digital transformation" really mean? Through integrating technology, such as internet of things, cloud-based intelligence and artificial intelligence to redefine the operating model, digital transformation allows organizations to reimagine their business operations to improve customer experience and meet emerging market demands. We've seen successful examples of digital transformations across many industries: Amazon's game-changing customer insight-driven platform and business model, DHL's automated stock management supply chain system, and GE's predictive analytic tools that measure equipment data for proactive maintenance. Even banking -- governed by strict security and legislation frameworks designed to protect customer privacy -- has evolved from branches to ATMs to mobile apps.

Many organizations have begun their data science journeys by starting "centers of excellence," hiring the best data scientists they can and focusing their efforts where there is lots of data. In some respects, this makes good sense -- after all, they don't want to be late to the artificial intelligence or machine learning party. Plus, data scientists want to show off their latest tools. But is this the best way to deploy this rare resource? For most companies, we think it unlikely.

With surmounting interest in data science and the fast-growing Data Scientist community, AI as a technology has come a long way crossing the chasm from Innovators and early adopters to the Early Majority. Along with all the hype that's there today around AI, there is still the unaddressed issue of less than 12% models reaching the production stage Data Scientists are creating models day in and day out but there are millions of models that are still waiting to see the light of the day in production. While the usual belief is that the deployment should need fewer days than building a model but it is becoming the most challenging issue of the industry today. Building the model is one thing, what's more, challenging is operationalizing AI. Analytics challenged leadership: This one serves as the major hurdle in operationalizing AI.

AI is humanity's new frontier. Once this boundary is crossed, AI will lead to a new form of human civilization. The guiding principle of AI is not to become autonomous or replace human intelligence. But we must ensure that it is developed through a humanist approach, based on values and human rights. We are faced with a crucial question: what kind of society do we want for tomorrow?

Whether it's Alexa ordering pizzas or Google rerouting commutes in real time, many of us live with -- even rely upon -- artificial intelligence in our daily lives. Yet when it comes to work, AI is still largely seen as a futuristic aspect of science fiction. But intelligent machines are already occupying many roles once performed by humans, from picking fruit to determining our credit scores, and a future where robots provide nursing services and self-driving cars shuttle us across town is not so far away. It won't just be workers on the front lines who need to adapt to the presence of AI and its impact on their jobs. Indeed, people all the way up the org chart need to evolve, including senior leaders.

In a 1967 McKinsey Quarterly article, "The manager and the moron," Peter Drucker noted that "the computer makes no decisions; it only carries out orders. It's a total moron, and therein lies its strength. It forces us to think, to set the criteria. The stupider the tool, the brighter the master has to be--and this is the dumbest tool we have ever had."1 1.Peter Drucker, "The manager and the moron," McKinsey Quarterly, 1967. After years of promise and hype, machine learning has at last hit the vertical part of the exponential curve.