DETROIT - Volkswagen AG and Ford Motor Co. said on Tuesday they will join forces on commercial vans and pickups and are exploring joint development of electric and self-driving technology in moves meant to save the automakers billions of dollars. Ford and VW announced their partnership against the backdrop of the Detroit auto show. The tie-up, which starts with sales of vans and medium-sized pickups in 2022, will not involve a merger or equity stakes, the companies said. "It is no secret that our industry is undergoing fundamental change, resulting from widespread electrification, ever stricter emission regulation, digitization, the shift towards autonomous driving, and not least the changing customer preferences," Volkswagen Chief Executive Herbert Diess told reporters and analysts on a conference call. "Carmakers around the globe therefore are investing heavily to align their portfolios to future needs and accelerate their innovation cycles," he added.

Machine learning and data analytics are powering a wave of ground-breaking technologies. Companies considering how to invest in artificial intelligence (AI) capabilities should first understand that over the coming five years, applications and machines will become less artificial and more intelligent. They will rely less on bottomup big data and more on topdown reasoning that more closely resembles the way humans approach problems and tasks. This general reasoning ability will enable AI to be more broadly applied than ever, creating opportunities for early adopters even in businesses and activities to which it previously seemed unsuited. Current AI has lots of limitations.

Greater collaboration across the Australian financial services industry is required in order to help financial advisers leverage the full potential of artificial intelligence and machine-learning technology, according to education provider Kaplan Professional and regtech pioneer Red Marker. Matt Symons, Red Marker CEO, said there needs to be a mindset change in how AI and machine-learning tools can best assist the industry. "It is important both dealer groups and vendors progress with realistic expectations, particularly around the'pre-work' that needs to be done to ensure financial advice can become an ideal candidate for automated solutions," Symons said. "If the financial services industry wants to increase the likelihood that effective statement of advice (SoA) review solutions emerge at a faster rate, then we need to come together and collaborate... working together is going to be key to developing highly reliable, automated review solutions." The two organisations said that before the industry could leverage AI and machine learning in financial advice, existing pre-conditions needed to be in place, including managing expectations, recognising the limitations in training data, and resolving diverging approaches to SoA construction, automatic programming language, and product comparison logic.

The past year has been a great one for AI and Machine Learning. Many new high-impact applications of Machine Learning were discovered and brought to light, especially in healthcare, finance, speech recognition, augmented reality, and more complex 3D and video applications. We've seen a big push towards more application driven research, rather than theoretical. Although this can have its drawbacks, it has for the time being made some great positive impacts, generating new R&D that can rapidly be turned into business and customer value. This trend is strongly reflected in much of the ML open source work.

A well-organized supply chain has always been a powerful source of competitive advantage. In today's interconnected economy it very essential. With the development of Artificial Intelligence (AI) solutions, logistics departments can solve many complex problems. For example, optimization, forecasting errors, reducing losses in sales caused by product unavailability, etc. This information is according to a recent paper by McKinsey Global Institute.

In the era of AI superpowers, Finland is no match for the US and China. So the Scandinavian country is taking a different tack. It has embarked on an ambitious challenge to teach the basics of AI to 1% of its population, or 55,000 people. Once it reaches that goal, it plans to go further, increasing the share of the population with AI know-how. The scheme is all part of a greater effort to establish Finland as a leader in applying and using the technology.

Ford and Volkswagen announced a non-equity partnership between the two carmakers today at the Auto Show in Detroit. The partnership, unlike the Nissan and Renault deal, will not involve the two companies taking ownership stakes in each other. The American and German car makers are planning on building commercial vans and medium-sized pickup trucks within the next four years together. The two also agreed to work together on self-driving vehicle research. According to Deloitte, across the globe, people are starting to trust the safety of autonomous vehicles.

This German expressionist film was not sophisticated (in fact, science fiction writer H.G. Wells said the movie was downright "silly") but it did have some merit. If nothing else, it started an entire genre of films exploring the freakish possibilities of artificial intelligence. Fast forward ninety-plus years, and we're living in a world where intelligent systems are more than a sci-fi fantasy – they are becoming a necessity. In the technology trifecta of hyperconverged compute, storage, and networking, intelligence across the IT environment represents a critical next phase of networking. That line of discussion, as well as my affinity for AI movies, got me thinking about the accelerated pace of change in automation technology, and how the role of networking has changed, particularly in regards to network awareness.

In practical terms, one of the main changes to the new release is Feature Contribution Calculation (FCC). This shows which features are most influential for a model's prediction on a particular data sample by working out how much each feature contributed to the model's score for that particular data sample. The developers say that FCC is particularly important when you initially have a lot of features in your historic data and you want to work out which features to use. Using too many features can reduce the model's performance and accuracy.

Privacy (and GDPR) – This is obviously at the top of the list. Even when the database does not include personal data, AI systems may progressively infer (or re-identify) personal data, which will then have to be processed adequately in accordance with all applicable data protection regulations. As for the Italian (and other) jurisdiction(s), GDPR principles will have to be taken into account, including purpose limitation, fairness and transparency, as well as privacy by default and privacy by design. This implies, among other things, completing a prior Data Protection Impact Assessment (DPIA) and setting up a data governance system, also including, for instance, adequate privacy notices that inform about the consequences of the data processing. Data transfer regulations will have to be taken into account carefully, also considering that in certain cloud scenarios it may not always be possible to determine exactly where the data resides in a given moment in time.